14.1.5.2 Unbalanced Datasets, Imbalanced Sample Sizes, Imbalanced Data, Long-Tailed Data

Chapter Contents (Back)
Imbalanced Data. Unbalanced Data. Learning. Long-Tailed Data.

Barandela, R., Sánchez, J.S., García, V., Rangel, E.,
Strategies for learning in class imbalance problems,
PR(36), No. 3, March 2003, pp. 849-851.
Elsevier DOI 0301
BibRef

Sun, Y.M.[Yan-Min], Kamel, M.S.[Mohamed S.], Wong, A.K.C.[Andrew K.C.], Wang, Y.[Yang],
Cost-sensitive boosting for classification of imbalanced data,
PR(40), No. 12, December 2007, pp. 3358-3378.
Elsevier DOI 0709
Classification; Class imbalance problem; AdaBoost; Cost-sensitive learning BibRef

Tang, Y., Zhang, Y.Q., Chawla, N.V., Krasser, S.,
SVMs Modeling for Highly Imbalanced Classification,
SMC-B(39), No. 1, February 2009, pp. 281-288.
IEEE DOI 0902
BibRef

Perez-Godoy, M.D.[Maria Dolores], Fernandez, A.[Alberto], Rivera, A.J.[Antonio Jesus], Jose del Jesus, M.[Maria],
Analysis of an evolutionary RBFN design algorithm, CO2RBFN, for imbalanced data sets,
PRL(31), No. 15, 1 November 2010, pp. 2375-2388.
Elsevier DOI 1003
Neural networks; Radial-basis function networks; Genetic algorithm; Imbalanced data sets; SMOTE pre-processing method BibRef

Fernandez-Navarro, F.[Francisco], Hervas-Martinez, C.[Cesar], Gutierrez, P.A.[Pedro Antonio],
A dynamic over-sampling procedure based on sensitivity for multi-class problems,
PR(44), No. 8, August 2011, pp. 1821-1833.
Elsevier DOI 1104
Classification; Multi-class; Sensitivity; Accuracy; Memetic algorithm; Imbalanced datasets; Over-sampling method; SMOTE BibRef

Soda, P.[Paolo],
A multi-objective optimisation approach for class imbalance learning,
PR(44), No. 8, August 2011, pp. 1801-1810.
Elsevier DOI 1104
Pattern recognition; Machine learning; Class imbalance learning; Multi-objective optimisation BibRef

Tahir, M.A.[Muhammad Atif], Kittler, J.V.[Josef V.], Bouridane, A.[Ahmed],
Multilabel classification using heterogeneous ensemble of multi-label classifiers,
PRL(33), No. 5, 1 April 2012, pp. 513-523.
Elsevier DOI 1202
Multilabel classification; Heterogeneous ensemble of multilabel classifiers; Static/dynamic weighting BibRef

Tahir, M.A.[Muhammad Atif], Kittler, J.V.[Josef V.], Yan, F.[Fei],
Inverse random under sampling for class imbalance problem and its application to multi-label classification,
PR(45), No. 10, October 2012, pp. 3738-3750.
Elsevier DOI 1206
Class imbalance problem; Multi-label classification; Inverse random under sampling BibRef

Thanathamathee, P.[Putthiporn], Lursinsap, C.[Chidchanok],
Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques,
PRL(34), No. 12, 1 September 2013, pp. 1339-1347.
Elsevier DOI 1306
Imbalanced data; Boundary data; Synthetic data generation; Bootstrap re-sampling; AdaBoost BibRef

Galar, M.[Mikel], Fernández, A.[Alberto], Barrenechea, E.[Edurne], Herrera, F.[Francisco],
EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling,
PR(46), No. 12, 2013, pp. 3460-3471.
Elsevier DOI 1308
Classification
See also Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers. BibRef

Maldonado, S.[Sebastián], López, J.[Julio],
Imbalanced data classification using second-order cone programming support vector machines,
PR(47), No. 5, 2014, pp. 2070-2079.
Elsevier DOI 1402
Class-imbalanced data BibRef

Shao, Y.H.[Yuan-Hai], Chen, W.J.[Wei-Jie], Zhang, J.J.[Jing-Jing], Wang, Z.[Zhen], Deng, N.Y.[Nai-Yang],
An efficient weighted Lagrangian twin support vector machine for imbalanced data classification,
PR(47), No. 9, 2014, pp. 3158-3167.
Elsevier DOI 1406
Imbalanced data classification BibRef

Sun, Z.B.[Zhong-Bin], Song, Q.B.[Qin-Bao], Zhu, X.Y.[Xiao-Yan], Sun, H.[Heli], Xu, B.[Baowen], Zhou, Y.M.[Yu-Ming],
A novel ensemble method for classifying imbalanced data,
PR(48), No. 5, 2015, pp. 1623-1637.
Elsevier DOI 1502
Imbalanced data BibRef

d'Addabbo, A.[Annarita], Maglietta, R.[Rosalia],
Parallel selective sampling method for imbalanced and large data classification,
PRL(62), No. 1, 2015, pp. 61-67.
Elsevier DOI 1507
Imbalanced learning BibRef

Cheng, F.Y.[Fan-Yong], Zhang, J.[Jing], Wen, C.H.[Cui-Hong],
Cost-Sensitive Large margin Distribution Machine for classification of imbalanced data,
PRL(80), No. 1, 2016, pp. 107-112.
Elsevier DOI 1609
Minimum margin BibRef

Zhang, X.G.[Xiao-Gang], Wang, D.X.[Ding-Xiang], Zhou, Y.C.[Yi-Cong], Chen, H.[Hua], Cheng, F.Y.[Fan-Yong], Liu, M.[Min],
Kernel modified optimal margin distribution machine for imbalanced data classification,
PRL(125), 2019, pp. 325-332.
Elsevier DOI 1909
Margin distribution, Imbalanced data classification, Kernel modification, Balanced detection rate, Generalization performance BibRef

Sáez, J.A.[José A.], Krawczyk, B.[Bartosz], Wozniak, M.[Michal],
Analyzing the oversampling of different classes and types of examples in multi-class imbalanced datasets,
PR(57), No. 1, 2016, pp. 164-178.
Elsevier DOI 1605
Machine learning BibRef

Vluymans, S.[Sarah], Tarragó, D.S.[Dánel Sánchez], Saeys, Y.[Yvan], Cornelis, C.[Chris], Herrera, F.[Francisco],
Fuzzy rough classifiers for class imbalanced multi-instance data,
PR(53), No. 1, 2016, pp. 36-45.
Elsevier DOI 1602
Multi-instance learning BibRef

Ng, W.W.Y.[Wing W.Y.], Zeng, G.J.[Guang-Jun], Zhang, J.J.[Jiang-Jun], Yeung, D.S.[Daniel S.], Pedrycz, W.[Witold],
Dual autoencoders features for imbalance classification problem,
PR(60), No. 1, 2016, pp. 875-889.
Elsevier DOI 1609
Imbalanced Classification BibRef

Zhang, X.Z.[Xiu-Zhen], Li, Y.X.[Yu-Xuan], Kotagiri, R.[Ramamohanarao], Wu, L.F.[Li-Fang], Tari, Z.[Zahir], Cheriet, M.[Mohamed],
KRNN: k Rare-class Nearest Neighbour classification,
PR(62), No. 1, 2017, pp. 33-44.
Elsevier DOI 1705
Imbalanced classification BibRef

Zhu, C.M.[Chang-Ming], Wang, Z.[Zhe],
Entropy-based matrix learning machine for imbalanced data sets,
PRL(88), No. 1, 2017, pp. 72-80.
Elsevier DOI 1703
Entropy BibRef

Xu, Y.,
Maximum Margin of Twin Spheres Support Vector Machine for Imbalanced Data Classification,
Cyber(47), No. 6, June 2017, pp. 1540-1550.
IEEE DOI 1706
Computational efficiency, Cybernetics, Kernel, Linear programming, Minimization, Quadratic programming, Support vector machines, Homocentric sphere, imbalanced data classification, maximum margin, maximum margin of twin spheres support vector machine (MMTSSVM), twin, support, vector, machine, (TSVM) BibRef

Gónzalez, S.[Sergio], García, S.[Salvador], Lázaro, M.[Marcelino], Figueiras-Vidal, A.R.[Aníbal R.], Herrera, F.[Francisco],
Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets,
PR(70), No. 1, 2017, pp. 12-24.
Elsevier DOI 1706
Imbalanced classification BibRef

Devi, D.[Debashree], Biswas, S.K.[Saroj K.], Purkayastha, B.[Biswajit],
Redundancy-driven modified Tomek-link based undersampling: A solution to class imbalance,
PRL(93), No. 1, 2017, pp. 3-12.
Elsevier DOI 1706
Data, mining BibRef

Tang, B.[Bo], He, H.B.[Hai-Bo],
GIR-based ensemble sampling approaches for imbalanced learning,
PR(71), No. 1, 2017, pp. 306-319.
Elsevier DOI 1707
Imbalanced, learning BibRef

Zhu, T.F.[Tuan-Fei], Lin, Y.P.[Ya-Ping], Liu, Y.[Yonghe],
Synthetic minority oversampling technique for multiclass imbalance problems,
PR(72), No. 1, 2017, pp. 327-340.
Elsevier DOI 1708
Multiclass imbalance problems BibRef

Ortigosa-Hernández, J.[Jonathan], Inza, I.[Iñaki], Lozano, J.A.[Jose A.],
Measuring the class-imbalance extent of multi-class problems,
PRL(98), No. 1, 2017, pp. 32-38.
Elsevier DOI 1710
Class-imbalance, problem BibRef

Kang, Q., Chen, X., Li, S., Zhou, M.,
A Noise-Filtered Under-Sampling Scheme for Imbalanced Classification,
Cyber(47), No. 12, December 2017, pp. 4263-4274.
IEEE DOI 1712
Approximation algorithms, Benchmark testing, Computers, Cybernetics, Data preprocessing, Noise measurement, Training, under-sampling BibRef

Castellanos, F.J.[Francisco J.], Valero-Mas, J.J.[Jose J.], Calvo-Zaragoza, J.[Jorge], Rico-Juan, J.R.[Juan R.],
Oversampling imbalanced data in the string space,
PRL(103), 2018, pp. 32-38.
Elsevier DOI 1802
Class imbalance problem, Oversampling, String space, SMOTE BibRef

Yuan, X.H.[Xiao-Hui], Xie, L.J.[Li-Jun], Abouelenien, M.[Mohamed],
A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data,
PR(77), 2018, pp. 160-172.
Elsevier DOI 1802
Ensemble, Deep learning, Imbalanced data, Cancer detection BibRef

Krawczyk, B.[Bartosz], McInnes, B.T.[Bridget T.],
Local ensemble learning from imbalanced and noisy data for word sense disambiguation,
PR(78), 2018, pp. 103-119.
Elsevier DOI 1804
Machine learning, Natural language processing, Imbalanced classification, Multi-class imbalance, Word sense disambiguation BibRef

Fernández-Baldera, A.[Antonio], Buenaposada, J.M.[José M.], Baumela, L.[Luis],
BAdaCost: Multi-class Boosting with Costs,
PR(79), 2018, pp. 467-479.
Elsevier DOI 1804
BibRef
Earlier:
Multi-class Boosting for Imbalanced Data,
IbPRIA15(57-64).
Springer DOI 1506
Boosting, Multi-class classification, Cost-sensitive classification, Multi-view object detection BibRef

Li, S.[Shuai], Song, W.F.[Wen-Feng], Qin, H.[Hong], Hao, A.[Aimin],
Deep variance network: An iterative, improved CNN framework for unbalanced training datasets,
PR(81), 2018, pp. 294-308.
Elsevier DOI 1806
Deep variance network, Unbalanced training datasets, Convolutional neural network, Homogeneity, Heterogeneity BibRef

Das, S.[Swagatam], Datta, S.[Shounak], Chaudhuri, B.B.[Bidyut B.],
Handling data irregularities in classification: Foundations, trends, and future challenges,
PR(81), 2018, pp. 674-693.
Elsevier DOI 1806
Data irregularities, Class imbalance, Small disjuncts, Class-distribution skew, Missing features, Absent features BibRef

Metzler, G.[Guillaume], Badiche, X.[Xavier], Belkasmi, B.[Brahim], Fromont, E.[Elisa], Habrard, A.[Amaury], Sebban, M.[Marc],
Learning maximum excluding ellipsoids from imbalanced data with theoretical guarantees,
PRL(112), 2018, pp. 310-316.
Elsevier DOI 1809
Imbalanced data, Classification, Metric learning, Statistical machine learning, Uniform stability, Support vector data description BibRef

Gautheron, L.[Leo], Habrard, A.[Amaury], Morvant, E.[Emilie], Sebban, M.[Marc],
Metric Learning from Imbalanced Data with Generalization Guarantees,
PRL(133), 2020, pp. 298-304.
Elsevier DOI 2005
Imbalanced Data, Classification, Metric Learning, Statistical Machine Learning, Uniform Stability BibRef

Zhu, R.[Rui], Wang, Z.[Ziyu], Ma, Z.Y.[Zhan-Yu], Wang, G.J.[Gui-Jin], Xue, J.H.[Jing-Hao],
LRID: A new metric of multi-class imbalance degree based on likelihood-ratio test,
PRL(116), 2018, pp. 36-42.
Elsevier DOI 1812
Imbalanced learning, Imbalance degree, Likelihood ratio, Class distribution BibRef

Luque, A.[Amalia], Carrasco, A.[Alejandro], Martín, A.[Alejandro], de las Heras, A.[Ana],
The impact of class imbalance in classification performance metrics based on the binary confusion matrix,
PR(91), 2019, pp. 216-231.
Elsevier DOI 1904
Classification, Performance measures, Imbalanced datasets, Class Balance Metrics BibRef

Zhou, G.J.[Guang-Jiao], Zhang, Y.[Ye],
Transfer and Association: A Novel Detection Method for Targets without Prior Homogeneous Samples,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907
Unbalanced data. BibRef

Sun, F.[Fei], Wang, R.[Run], Wan, B.[Bo], Su, Y.J.[Yan-Jun], Guo, Q.H.[Qing-Hua], Huang, Y.X.[You-Xin], Wu, X.C.[Xin-Cai],
Efficiency of Extreme Gradient Boosting for Imbalanced Land Cover Classification Using an Extended Margin and Disagreement Performance,
IJGI(8), No. 7, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Sadhukhan, P.[Payel], Palit, S.[Sarbani],
Reverse-nearest neighborhood based oversampling for imbalanced, multi-label datasets,
PRL(125), 2019, pp. 813-820.
Elsevier DOI 1909
Reverse nearest neighborhood, Multi-label classification, Multi-label learning, Class-imbalance, Oversampling BibRef

Indraswari, R.[Rarasmaya], Kurita, T.[Takio], Arifin, A.Z.[Agus Zainal], Suciati, N.[Nanik], Astuti, E.R.[Eha Renwi],
Multi-projection deep learning network for segmentation of 3D medical images,
PRL(125), 2019, pp. 791-797.
Elsevier DOI 1909
Deep learning, Image segmentation, Imbalanced dataset, Neural networks, Three-dimensional medical image BibRef

Kim, Y.G.[Young-Geun], Kwon, Y.C.[Yong-Chan], Paik, M.C.[Myunghee Cho],
Valid oversampling schemes to handle imbalance,
PRL(125), 2019, pp. 661-667.
Elsevier DOI 1909
Imbalance, Oversampling, Optimal oversampling target proportion, Resampling at random, Medical imaging BibRef

Kaur, H.[Harsurinder], Pannu, H.S.[Husanbir Singh], Malhi, A.K.[Avleen Kaur],
A Systematic Review on Imbalanced Data Challenges in Machine Learning: Applications and Solutions,
Surveys(52), No. 4, September 2019, pp. Article No 79.
DOI Link 1912
Survey, Imbalanced Data. BibRef

Douzas, G.[Georgios], Bacao, F.[Fernando], Fonseca, J.[Joao], Khudinyan, M.[Manvel],
Imbalanced Learning in Land Cover Classification: Improving Minority Classes' Prediction Accuracy Using the Geometric SMOTE Algorithm,
RS(11), No. 24, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Koziarski, M.[Michal],
Radial-Based Undersampling for imbalanced data classification,
PR(102), 2020, pp. 107262.
Elsevier DOI 2003
Machine learning, Classification, Imbalanced data, Undersampling, Radial basis functions BibRef

Mullick, S.S.[Sankha Subhra], Datta, S.[Shounak], Dhekane, S.G.[Sourish Gunesh], Das, S.[Swagatam],
Appropriateness of performance indices for imbalanced data classification: An analysis,
PR(102), 2020, pp. 107197.
Elsevier DOI 2003
Imbalanced classification, Performance evaluation indices, Precision, Recall, GMean, Area under the curve BibRef

Richhariya, B., Tanveer, M.,
A reduced universum twin support vector machine for class imbalance learning,
PR(102), 2020, pp. 107150.
Elsevier DOI 2003
Universum, Rectangular kernel, Class imbalance, Imbalance ratio, Twin support vector machine BibRef

Shi, C.H.[Cang-Hong], Li, X.J.[Xiao-Jie], Lv, J.C.[Jian-Cheng], Yin, J.[Jing], Mumtaz, I.[Imran],
Robust geodesic based outlier detection for class imbalance problem,
PRL(131), 2020, pp. 428-434.
Elsevier DOI 2004
Outlier detection, Structural stability, Local structure BibRef

Zhu, R.[Rui], Guo, Y.[Yiwen], Xue, J.H.[Jing-Hao],
Adjusting the imbalance ratio by the dimensionality of imbalanced data,
PRL(133), 2020, pp. 217-223.
Elsevier DOI 2005
Imbalanced data, Imbalance extent, Imbalanced learning, Imbalance ratio, Pearson correlation test BibRef

Huang, C.X.[Chen-Xi], Huang, X.[Xin], Fang, Y.[Yu], Xu, J.F.[Jian-Feng], Qu, Y.[Yi], Zhai, P.J.[Peng-Jun], Fan, L.[Lin], Yin, H.[Hua], Xu, Y.[Yilu], Li, J.H.[Jia-Hang],
Sample imbalance disease classification model based on association rule feature selection,
PRL(133), 2020, pp. 280-286.
Elsevier DOI 2005
Association rules, Feature selection, Integrated learning, Sample imbalance BibRef

Xiao, G.B.[Guo-Bao], Zhou, X.[Xiong], Yan, Y.[Yan], Wang, H.Z.[Han-Zi],
A two-step hypergraph reduction based fitting method for unbalanced data,
PRL(134), 2020, pp. 106-115.
Elsevier DOI 2005
Hypergraph reduction, Hypergraph construction, Unbalanced data, Model fitting BibRef

Jimenez-Castaño, C., Alvarez-Meza, A., Orozco-Gutierrez, A.,
Enhanced automatic twin support vector machine for imbalanced data classification,
PR(107), 2020, pp. 107442.
Elsevier DOI 2008
Imbalanced data, Kernel methods, Twin support vector machines BibRef

Wang, C.[Chen], Deng, C.Y.[Cheng-Yuan], Wang, S.Z.[Su-Zhen],
Imbalance-XGBoost: leveraging weighted and focal losses for binary label-imbalanced classification with XGBoost,
PRL(136), 2020, pp. 190-197.
Elsevier DOI 2008
Imbalanced classification, XGBoost, Python package BibRef

Sadhukhan, P.[Payel], Palit, S.[Sarbani],
Adaptive learning of minority class prior to minority oversampling,
PRL(136), 2020, pp. 16-24.
Elsevier DOI 2008
Class imbalance, Relative neighborhood graph, Minority set estimation, Oversampling BibRef

Santos, M.S.[Miriam Seoane], Abreu, P.H.[Pedro Henriques], Wilk, S.[Szymon], Santos, J.[João],
How distance metrics influence missing data imputation with k-nearest neighbours,
PRL(136), 2020, pp. 111-119.
Elsevier DOI 2008
Missing Data, Data Imputation, k-nearest neighbours, Distance Functions, Heterogeneous Data, Imbalanced Data BibRef

Zhu, Q.[Qiuming],
On the performance of Matthews correlation coefficient (MCC) for imbalanced dataset,
PRL(136), 2020, pp. 71-80.
Elsevier DOI 2008
Matthews correlation coefficient, Classification accuracy measurement, Performance evaluation, Imbalanced dataset BibRef

Gao, Y.L.[Yun-Long], Yang, C.Y.[Cheng-Yu], Lin, K.Y.[Kuo-Yi], Pan, J.Y.[Jin-Yan], Li, L.[Li],
Conditional semi-fuzzy c-means clustering for imbalanced dataset,
IET-IPR(14), No. 11, September 2020, pp. 2343-2355.
DOI Link 2009
BibRef

Naboureh, A.[Amin], Li, A.[Ainong], Bian, J.H.[Jin-Hu], Lei, G.[Guangbin], Amani, M.[Meisam],
A Hybrid Data Balancing Method for Classification of Imbalanced Training Data within Google Earth Engine: Case Studies from Mountainous Regions,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010
BibRef

Naboureh, A.[Amin], Ebrahimy, H.[Hamid], Azadbakht, M.[Mohsen], Bian, J.H.[Jin-Hu], Amani, M.[Meisam],
RUESVMs: An Ensemble Method to Handle the Class Imbalance Problem in Land Cover Mapping Using Google Earth Engine,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011
BibRef

Jing, X.Y.[Xiao-Yuan], Zhang, X.Y.[Xin-Yu], Zhu, X.K.[Xiao-Ke], Wu, F.[Fei], You, X.G.[Xin-Ge], Gao, Y.[Yang], Shan, S.G.[Shi-Guang], Yang, J.Y.[Jing-Yu],
Multiset Feature Learning for Highly Imbalanced Data Classification,
PAMI(43), No. 1, January 2021, pp. 139-156.
IEEE DOI 2012
Learning systems, Measurement, Task analysis, Correlation, Training, Usability, Generative adversarial networks, weighted uncorrelated constraint BibRef

Quan, Y.H.[Ying-Hui], Zhong, X.[Xian], Feng, W.[Wei], Chan, J.C.W.[Jonathan Cheung-Wai], Li, Q.[Qiang], Xing, M.D.[Meng-Dao],
SMOTE-Based Weighted Deep Rotation Forest for the Imbalanced Hyperspectral Data Classification,
RS(13), No. 3, 2021, pp. xx-yy.
DOI Link 2102
BibRef

Yuan, M.[Min], Xu, Y.T.[Yi-Tian],
Bound estimation-based safe acceleration for maximum margin of twin spheres machine with pinball loss,
PR(114), 2021, pp. 107860.
Elsevier DOI 2103
Maximum margin, Pinball loss, Imbalanced data, Bound estimation, Upper and lower bounds BibRef

Zhu, Y.[Ye], Ting, K.M.[Kai Ming], Carman, M.J.[Mark J.], Angelova, M.[Maia],
CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities,
PR(117), 2021, pp. 107977.
Elsevier DOI 2106
Density-ratio, Density-based clustering, NN Anomaly detection, Inhomogeneous cluster densities, Scaling, Shift BibRef

Raghuwanshi, B.S.[Bhagat Singh], Shukla, S.[Sanyam],
Minimum variance-embedded kernelized extension of extreme learning machine for imbalance learning,
PR(119), 2021, pp. 108069.
Elsevier DOI 2106
Extreme learning machine, Minimum variance-embedded class-specific kernelized extreme learning machine, Classification BibRef

Bejaoui, A.[Amine], Elkhalil, K.[Khalil], Kammoun, A.[Abla], Alouini, M.S.[Mohamed-Slim], Al-Naffouri, T.[Tareq],
Cost-sensitive design of quadratic discriminant analysis for imbalanced data,
PRL(149), 2021, pp. 24-29.
Elsevier DOI 2108
Quadratic discriminant analysis, Random matrix theory, Classification, Imbalanced learning BibRef

Kim, K.[Kyoungok],
Normalized class coherence change-based kNN for classification of imbalanced data,
PR(120), 2021, pp. 108126.
Elsevier DOI 2109
NN, Nearest neighbor classification, Imbalanced data, Class coherence BibRef

Koziarski, M.[Michal],
Potential Anchoring for imbalanced data classification,
PR(120), 2021, pp. 108114.
Elsevier DOI 2109
Machine learning, Classification, Imbalanced data, Oversampling, Undersampling, Radial basis functions BibRef

Jing, T.T.[Tao-Tao], Xu, B.R.[Bing-Rong], Ding, Z.M.[Zheng-Ming],
Towards Fair Knowledge Transfer for Imbalanced Domain Adaptation,
IP(30), 2021, pp. 8200-8211.
IEEE DOI 2110
Training, Task analysis, Adaptation models, Knowledge transfer, Data models, Training data, Optimization, Transfer learning, unsupervised domain adaptation BibRef

Kim, Y.C.[Ye-Chan], Lee, Y.[Younkwan], Jeon, M.[Moongu],
Imbalanced image classification with complement cross entropy,
PRL(151), 2021, pp. 33-40.
Elsevier DOI 2110
Loss function, Deep learning, Class imbalance, Image classification, Complement cross entropy BibRef

Liu, B.[Bin], Blekas, K.[Konstantinos], Tsoumakas, G.[Grigorios],
Multi-label sampling based on local label imbalance,
PR(122), 2022, pp. 108294.
Elsevier DOI 2112
Multi-label learning, Class imbalance, Oversampling and undersampling, Local label imbalance, Ensemble methods BibRef

Suh, S.[Sungho], Lukowicz, P.[Paul], Lee, Y.O.[Yong Oh],
Discriminative feature generation for classification of imbalanced data,
PR(122), 2022, pp. 108302.
Elsevier DOI 2112
Imbalanced classification, Generative adversarial networks, Discriminative feature generation, Transfer learning, Feature map regularization BibRef

Maldonado, S.[Sebastián], Vairetti, C.[Carla], Fernandez, A.[Alberto], Herrera, F.[Francisco],
FW-SMOTE: A feature-weighted oversampling approach for imbalanced classification,
PR(124), 2022, pp. 108511.
Elsevier DOI 2203
Data resampling, SMOTE, OWA Operators, Feature selection, Imbalanced data classification BibRef

Hao, J.Y.[Jing-Yu], Wang, C.J.[Cheng-Jia], Yang, G.[Guang], Gao, Z.[Zhifan], Zhang, J.L.[Jing-Lin], Zhang, H.[Heye],
Annealing Genetic GAN for Imbalanced Web Data Learning,
MultMed(24), 2022, pp. 1164-1174.
IEEE DOI 2203
Training, Generators, Genetic algorithms, Annealing, Simulated annealing, Generative adversarial networks, data augmentation BibRef

Wang, Z.[Zhe], Dong, Q.[Qida], Guo, W.[Wei], Li, D.D.[Dong-Dong], Zhang, J.[Jing], Du, W.L.[Wen-Li],
Geometric imbalanced deep learning with feature scaling and boundary sample mining,
PR(126), 2022, pp. 108564.
Elsevier DOI 2204
Imbalance problem, Image classification, Geometric information, Boundary samples mining, Feature scaling BibRef

Gilet, C.[Cyprien], Barbosa, S.[Susana], Fillatre, L.[Lionel],
Discrete Box-Constrained Minimax Classifier for Uncertain and Imbalanced Class Proportions,
PAMI(44), No. 6, June 2022, pp. 2923-2937.
IEEE DOI 2205
Training, Task analysis, Bayes methods, Robustness, Equalizers, Medical diagnostic imaging, Support vector machines, Bayesian robustness BibRef

Sridhar, S., Kalaivani, A.,
Performance Analysis of Two-Stage Iterative Ensemble Method over Random Oversampling Methods on Multiclass Imbalanced Datasets,
IJIG(22), No. 2, April 2022, pp. 2250025.
DOI Link 2205
BibRef

Wang, G.J.[Guan-Jin], Zhou, T.[Ta], Choi, K.S.[Kup-Sze], Lu, J.[Jie],
A Deep-Ensemble-Level-Based Interpretable Takagi-Sugeno-Kang Fuzzy Classifier for Imbalanced Data,
Cyber(52), No. 5, May 2022, pp. 3805-3818.
IEEE DOI 2206
Training, Cancer, Task analysis, Machine learning, Cybernetics, Prediction algorithms, Information technology, imbalance learning BibRef

Chen, J.[Joya], Liu, D.[Dong], Luo, B.[Bin], Peng, X.Z.[Xue-Zheng], Xu, T.[Tong], Chen, E.[Enhong],
Residual objectness for imbalance reduction,
PR(130), 2022, pp. 108781.
Elsevier DOI 2206
Object detection, Class imbalance, Residual objectness BibRef

Quan, D.Y.[Da-Ying], Feng, W.[Wei], Dauphin, G.[Gabriel], Wang, X.F.[Xiao-Feng], Huang, W.J.[Wen-Jiang], Xing, M.D.[Meng-Dao],
A Novel Double Ensemble Algorithm for the Classification of Multi-Class Imbalanced Hyperspectral Data,
RS(14), No. 15, 2022, pp. xx-yy.
DOI Link 2208
BibRef

Sinha, S.[Saptarshi], Ohashi, H.[Hiroki], Nakamura, K.[Katsuyuki],
Class-Difficulty Based Methods for Long-Tailed Visual Recognition,
IJCV(130), No. 10, October 2022, pp. 2517-2531.
Springer DOI 2209
BibRef
Earlier:
Class-wise Difficulty-balanced Loss for Solving Class-imbalance,
ACCV20(VI:549-565).
Springer DOI 2103
BibRef

Sinha, S.[Saptarshi], Ohashi, H.[Hiroki],
Difficulty-Net: Learning to Predict Difficulty for Long-Tailed Recognition,
WACV23(6433-6442)
IEEE DOI 2302
Training, Codes, Tail, Predictive models, Algorithms: Machine learning architectures, formulations, ethical computer vision BibRef

Chen, L.[Ling], Wang, Y.H.[Yu-Hong], Li, H.[Hao],
Enhancement of DNN-based multilabel classification by grouping labels based on data imbalance and label correlation,
PR(132), 2022, pp. 108964.
Elsevier DOI 2209
Multilabel classification, data imbalance, label correlation, neural network BibRef

Zhao, L.C.[Lin-Chang], Shang, Z.W.[Zhao-Wei], Tan, J.[Jin], Zhou, M.L.[Ming-Liang], Zhang, M.[Mu], Gu, D.D.[David Dagang], Zhang, T.P.[Tai-Ping], Tang, Y.Y.[Yuan Yan],
Siamese networks with an online reweighted example for imbalanced data learning,
PR(132), 2022, pp. 108947.
Elsevier DOI 2209
Few-shot learning, Reweighted example learning, Data mining, Imbalanced learning BibRef

Wang, W.Q.[Wei-Qiu], Zhao, Z.C.[Zhi-Cheng], Wang, P.[Pingyu], Su, F.[Fei], Meng, H.Y.[Hong-Ying],
Attentive Feature Augmentation for Long-Tailed Visual Recognition,
CirSysVideo(32), No. 9, September 2022, pp. 5803-5816.
IEEE DOI 2209
Visualization, Head, Image recognition, Task analysis, Feature extraction, Data models, Training, Image classification, data synthesizing BibRef

Zhang, M.L.[Ming-Liang], Zhang, X.Y.[Xu-Yao], Wang, C.[Chuang], Liu, C.L.[Cheng-Lin],
Towards prior gap and representation gap for long-tailed recognition,
PR(133), 2023, pp. 109012.
Elsevier DOI 2210
Long-tailed learning, Prior gap, Representation gap, Image recognition BibRef

Liu, Y.X.[Yong-Xu], Liu, Y.[Yan], Yu, B.X.B.[Bruce X.B.], Zhong, S.H.[Sheng-Hua], Hu, Z.J.[Zhe-Jing],
Noise-robust oversampling for imbalanced data classification,
PR(133), 2023, pp. 109008.
Elsevier DOI 2210
Imbalanced learning, Classification, Clustering BibRef

Ren, J.J.[Jin-Jun], Wang, Y.P.[Yu-Ping], Cheung, Y.M.[Yiu-Ming], Gao, X.Z.[Xiao-Zhi], Guo, X.F.[Xiao-Fang],
Grouping-based Oversampling in Kernel Space for Imbalanced Data Classification,
PR(133), 2023, pp. 108992.
Elsevier DOI 2210
Imbalanced data classification, Kernel method, Support vector machine, Oversampling BibRef

Liu, C.L.[Chien-Liang], Chang, Y.H.[Yu-Hua],
Learning From Imbalanced Data With Deep Density Hybrid Sampling,
SMCS(52), No. 11, November 2022, pp. 7065-7077.
IEEE DOI 2210
Boosting, Training, Euclidean distance, Sampling methods, Costs, Hybrid power systems, Estimation, Class imbalance, synthetic data BibRef

Datta, D.[Debaleena], Mallick, P.K.[Pradeep Kumar], Reddy, A.V.N.[Annapareddy V. N.], Mohammed, M.A.[Mazin Abed], Jaber, M.M.[Mustafa Musa], Alghawli, A.S.[Abed Saif], Al-Qaness, M.A.A.[Mohammed A. A.],
A Hybrid Classification of Imbalanced Hyperspectral Images Using ADASYN and Enhanced Deep Subsampled Multi-Grained Cascaded Forest,
RS(14), No. 19, 2022, pp. xx-yy.
DOI Link 2210
BibRef

Zhu, Q.Q.[Qi-Qi], Deng, W.H.[Wei-Huan], Zheng, Z.[Zhuo], Zhong, Y.F.[Yan-Fei], Guan, Q.F.[Qing-Feng], Lin, W.H.[Wei-Hua], Zhang, L.P.[Liang-Pei], Li, D.R.[De-Ren],
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification,
Cyber(52), No. 11, November 2022, pp. 11709-11723.
IEEE DOI 2211
Feature extraction, Training, Field programmable gate arrays, Data mining, Hyperspectral imaging, Deep learning, Convolution, patchwise BibRef

Rodríguez-Alvarez, Y.[Yanela], García-Lorenzo, M.M.[María Matilde], Caballero-Mota, Y.[Yailé], Filiberto-Cabrera, Y.[Yaima], García-Hilarión, I.M.[Isabel M.], Machado-Montes-de Oca, D.[Daniela], Bello Pérez, R.[Rafael],
Fuzzy prototype selection-based classifiers for imbalanced data. Case study,
PRL(163), 2022, pp. 183-190.
Elsevier DOI 2212
Fuzzy learning, Prototype classifiers, Imbalanced Data BibRef

Gutiérrez-López, A.[Aitor], González-Serrano, F.J.[Francisco-Javier], Figueiras-Vidal, A.R.[Aníbal R.],
Optimum Bayesian thresholds for rebalanced classification problems using class-switching ensembles,
PR(135), 2023, pp. 109158.
Elsevier DOI 2212
Bayesian framework, Ensembles, Rebalancing techniques, Imbalanced classification, Label switching BibRef

Naji, H.A.H.[Hasan A. H.], Li, T.F.[Tian-Feng], Xue, Q.J.[Qing-Ji], Duan, X.D.[Xin-Dong],
A Hypered Deep-Learning-Based Model of Hyperspectral Images Generation and Classification for Imbalanced Data,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

Shaik, A.L.H.P.[Abdul Lateef Haroon Phulara], Manoharan, M.K.[Monica Komala], Pani, A.K.[Alok Kumar], Avala, R.R.[Raji Reddy], Chen, C.M.[Chien-Ming],
Gaussian Mutation-Spider Monkey Optimization (GM-SMO) Model for Remote Sensing Scene Classification,
RS(14), No. 24, 2022, pp. xx-yy.
DOI Link 2212
BibRef

van Duynhoven, A.[Alysha], Dragicevic, S.[Suzana],
Mitigating Imbalance of Land Cover Change Data for Deep Learning Models with Temporal and Spatiotemporal Sample Weighting Schemes,
IJGI(11), No. 12, 2022, pp. xx-yy.
DOI Link 2301
BibRef

Cao, C.Z.[Chun-Zheng], Liu, X.[Xin], Cao, S.[Shuren], Shi, J.Q.[Jian Qing],
Joint classification and prediction of random curves using heavy-tailed process functional regression,
PR(136), 2023, pp. 109213.
Elsevier DOI 2301
Functional data analysis, Outliers, Heavy-tailed process, Bayesian estimation, MCMC BibRef

Liu, J.H.[Jia-Hang], Feng, R.[Ruilei], Chen, P.[Peng], Wang, X.Z.[Xiao-Zhen], Ni, Y.[Yue],
Dynamic Loss Reweighting Method Based on Cumulative Classification Scores for Long-Tailed Remote Sensing Image Classification,
RS(15), No. 2, 2023, pp. xx-yy.
DOI Link 2301
BibRef

Wang, X.Y.[Xin-Yue], Jing, L.P.[Li-Ping], Lyu, Y.[Yilin], Guo, M.Z.[Ming-Zhe], Wang, J.Q.[Jia-Qi], Liu, H.F.[Hua-Feng], Yu, J.[Jian], Zeng, T.Y.[Tie-Yong],
Deep Generative Mixture Model for Robust Imbalance Classification,
PAMI(45), No. 3, March 2023, pp. 2897-2912.
IEEE DOI 2302
BibRef
Earlier: A1, A3, A2, Only:
Deep Generative Model for Robust Imbalance Classification,
CVPR20(14112-14121)
IEEE DOI 2008
Perturbation methods, Data models, Uncertainty, Codes, Training, Predictive models, Training data, Deep generative mixture model, model perturbation. Data models. BibRef

Cui, J.Q.[Jie-Quan], Liu, S.[Shu], Tian, Z.T.[Zhuo-Tao], Zhong, Z.S.[Zhi-Sheng], Jia, J.Y.[Jia-Ya],
ResLT: Residual Learning for Long-Tailed Recognition,
PAMI(45), No. 3, March 2023, pp. 3695-3706.
IEEE DOI 2302
Tail, Head, Training, Magnetic heads, Image recognition, Transfer learning, Representation learning, Residual learning, long-tailed recognition BibRef

Lázaro, M.[Marcelino], Figueiras-Vidal, A.R.[Aníbal R.],
Neural network for ordinal classification of imbalanced data by minimizing a Bayesian cost,
PR(137), 2023, pp. 109303.
Elsevier DOI 2302
Bayes cost, Parzen windows, Ordinal classification, Imbalanced BibRef

Wang, X.N.[Xin-Ning], Zhao, Y.[Yuben], Li, C.[Chong], Ren, P.[Peng],
ProbSAP: A comprehensive and high-performance system for student academic performance prediction,
PR(137), 2023, pp. 109309.
Elsevier DOI 2302
Student academic performance, SAP prediction, Educational data mining (EDM), Imbalanced data management, XGBoost-Enhanced method BibRef

Liu, Y.C.[Yan-Chen], Lai, K.W.C.[King Wai Chiu],
The Performance Index of Convolutional Neural Network-Based Classifiers in Class Imbalance Problem,
PR(137), 2023, pp. 109284.
Elsevier DOI 2302
Deep Learning, Convolutional Neural Network, Class Imbalance, Class Balance Index, Model Performance Index BibRef

Zhao, X.Q.[Xin-Qiao], Xiao, J.[Jimin], Yu, S.Y.[Si-Yue], Li, H.[Hui], Zhang, B.F.[Bing-Feng],
Weight-guided class complementing for long-tailed image recognition,
PR(138), 2023, pp. 109374.
Elsevier DOI 2303
Image recognition, Long-tailed distribution, Gradient shift, Weight-guided method BibRef

Li, M.[Mengke], Cheung, Y.M.[Yiu-Ming], Hu, Z.[Zhikai],
Key Point Sensitive Loss for Long-Tailed Visual Recognition,
PAMI(45), No. 4, April 2023, pp. 4812-4825.
IEEE DOI 2303
Tail, Training, Head, Optimization, Visualization, Magnetic heads, Training data, Long-tailed classification, imbalance learning BibRef

Xi, B.[Bobo], Li, J.J.[Jiao-Jiao], Diao, Y.[Yan], Li, Y.S.[Yun-Song], Li, Z.[Zan], Huang, Y.[Yan], Chanussot, J.[Jocelyn],
DGSSC: A Deep Generative Spectral-Spatial Classifier for Imbalanced Hyperspectral Imagery,
CirSysVideo(33), No. 4, April 2023, pp. 1535-1548.
IEEE DOI 2304
Training, Hyperspectral imaging, Feature extraction, Data models, Decoding, Benchmark testing, hyperspectral image classification BibRef

Tiong, A.M.H.[Anthony Meng Huat], Li, J.[Junnan], Lin, G.S.[Guo-Sheng], Li, B.Y.[Bo-Yang], Xiong, C.M.[Cai-Ming], Hoi, S.C.H.[Steven C.H.],
Improving Tail-Class Representation with Centroid Contrastive Learning,
PRL(168), 2023, pp. 123-130.
Elsevier DOI 2304
Long-tailed classification, Imbalanced learning, Contrastive learning, Deep learning BibRef

Chen, H.[Huanfa], Cheng, Y.[Yan],
Travel Mode Choice Prediction Using Imbalanced Machine Learning,
ITS(24), No. 4, April 2023, pp. 3795-3808.
IEEE DOI 2304
Predictive models, Machine learning, Measurement, Support vector machines, Testing, Neural networks, Data models, travel mode choice BibRef

Xiang, L.[Liuyu], Han, J.G.[Jun-Gong], Ding, G.[Guiguang],
Margin-aware rectified augmentation for long-tailed recognition,
PR(141), 2023, pp. 109608.
Elsevier DOI 2306
Long-tailed recognition, Data augmentation, Mixup BibRef

Wang, S.[Shuang], Chen, H.[Hui], Ding, L.[Lei], Sui, H.[He], Ding, J.L.[Jian-Li],
GAN-SR Anomaly Detection Model Based on Imbalanced Data,
IEICE(E106-D), No. 7, July 2023, pp. 1209-1218.
WWW Link. 2307
BibRef

Rosales-Pérez, A.[Alejandro], García, S.[Salvador], Herrera, F.[Francisco],
Handling Imbalanced Classification Problems With Support Vector Machines via Evolutionary Bilevel Optimization,
Cyber(53), No. 8, August 2023, pp. 4735-4747.
IEEE DOI 2307
Optimization, Support vector machines, Costs, Kernel, Training, Search problems, Inference algorithms, support vector machines (SVMs) BibRef

Islam, M.T.[Md Touhid], Islam, M.R.[Md Rashedul], Uddin, M.P.[Md Palash], Ulhaq, A.[Anwaar],
A Deep Learning-Based Hyperspectral Object Classification Approach via Imbalanced Training Samples Handling,
RS(15), No. 14, 2023, pp. 3532.
DOI Link 2307
BibRef

Gong, H.Y.[Hui-Yun], Li, Y.G.[Ye-Guang], Dong, J.[Jian],
A dual-balanced network for long-tail distribution object detection,
IET-CV(17), No. 5, 2023, pp. 565-575.
DOI Link 2309
computer vision, learning (artificial intelligence), object detection BibRef

Guan, Q.J.[Qing-Ji], Li, Z.Z.[Zhuang-Zhuang], Zhang, J.[Jiayu], Huang, Y.P.[Ya-Ping], Zhao, Y.[Yao],
Joint representation and classifier learning for long-tailed image classification,
IVC(137), 2023, pp. 104759.
Elsevier DOI 2309
Long-tailed image classification, Representation learning, Classifier learning, Supervised contrastive learning BibRef

Kim, D.J.[Dong-Jin], Ke, T.W.[Tsung-Wei], Yu, S.X.[Stella X.],
Local pseudo-attributes for long-tailed recognition,
PRL(172), 2023, pp. 51-57.
Elsevier DOI 2309
Long-tailed recognition, Pseudo-attributes, Self-supervised learning BibRef

Zhang, Y.F.[Yi-Fan], Kang, B.Y.[Bing-Yi], Hooi, B.[Bryan], Yan, S.C.[Shui-Cheng], Feng, J.S.[Jia-Shi],
Deep Long-Tailed Learning: A Survey,
PAMI(45), No. 9, September 2023, pp. 10795-10816.
IEEE DOI 2309
Survey, Long-Tailed. BibRef

Tan, J.[Jingru], Li, B.[Bo], Lu, X.[Xin], Yao, Y.Q.[Yong-Qiang], Yu, F.W.[Feng-Wei], He, T.[Tong], Ouyang, W.L.[Wan-Li],
The Equalization Losses: Gradient-Driven Training for Long-tailed Object Recognition,
PAMI(45), No. 11, November 2023, pp. 13876-13892.
IEEE DOI 2310
BibRef

Yang, J.X.[Jia-Xin], Yu, M.M.[Miao-Miao], Li, S.[Shuohao], Zhang, J.[Jun], Hu, S.Z.[Sheng-Ze],
Long-Tailed Object Detection for Multimodal Remote Sensing Images,
RS(15), No. 18, 2023, pp. 4539.
DOI Link 2310
BibRef

Jin, L.B.[Lian-Bao], Lei, D.Y.L.N.[Da-Yu Lang Na],
An Optimal Transport View of Class-Imbalanced Visual Recognition,
IJCV(131), No. 1, January 2023, pp. 2845-2863.
Springer DOI 2310
BibRef

Soltanzadeh, P.[Paria], Feizi-Derakhshi, M.R.[M. Reza], Hashemzadeh, M.[Mahdi],
Addressing the class-imbalance and class-overlap problems by a metaheuristic-based under-sampling approach,
PR(143), 2023, pp. 109721.
Elsevier DOI 2310
Imbalanced classification, Imbalanced datasets, Class overlap, Class imbalance, Metaheuristic algorithms, Under-sampling BibRef

Kong, X.Y.[Xiang-Yuan], Wei, X.[Xiang], Liu, X.Y.[Xiao-Yu], Wang, J.J.[Jing-Jie], Xing, W.W.[Wei-Wei], Lu, W.[Wei],
FGBC: Flexible graph-based balanced classifier for class-imbalanced semi-supervised learning,
PR(143), 2023, pp. 109793.
Elsevier DOI 2310
Semi-supervised learning, Class-imbalanced learning, Graph network, Label propagation, MixUp BibRef

Sun, J.[Junyao], Zhou, J.K.[Jing-Kai], Liu, Q.[Qiong],
PoiseNet: Dealing With Data Imbalance in DensePose,
CirSysVideo(33), No. 10, October 2023, pp. 5664-5678.
IEEE DOI 2310
BibRef

Zhou, X.S.[Xue-Song], Zhai, J.H.[Jun-Hai], Cao, Y.[Yang],
Feature fusion network for long-tailed visual recognition,
PR(144), 2023, pp. 109827.
Elsevier DOI 2310
Long-tailed learning, Head and tail classes, Feature representations, Feature fusion network BibRef

Liu, W.[Weide], Wu, Z.H.[Zhong-Hua], Wang, Y.M.[Yi-Ming], Ding, H.H.[Heng-Hui], Liu, F.[Fayao], Lin, J.[Jie], Lin, G.S.[Guo-Sheng],
LCReg: Long-tailed image classification with Latent Categories based Recognition,
PR(145), 2024, pp. 109971.
Elsevier DOI 2311
Long-tailed, Image classification, Latent Categories BibRef

Zhao, W.[Wei], Zhao, H.[Hong],
Hierarchical long-tailed classification based on multi-granularity knowledge transfer driven by multi-scale feature fusion,
PR(145), 2024, pp. 109842.
Elsevier DOI 2311
Long-tailed learning, Hierarchical classification, Multi-granularity, Multi-scale feature fusion, Knowledge transfer BibRef

Alexandridis, K.P.[Konstantinos Panagiotis], Luo, S.[Shan], Nguyen, A.[Anh], Deng, J.K.[Jian-Kang], Zafeiriou, S.[Stefanos],
Inverse Image Frequency for Long-Tailed Image Recognition,
IP(32), 2023, pp. 5721-5736.
IEEE DOI Code:
WWW Link. 2311
BibRef

Tan, Z.C.[Zi-Chang], Li, J.[Jun], Du, J.[Jinhao], Wan, J.[Jun], Lei, Z.[Zhen], Guo, G.D.[Guo-Dong],
NCL++: Nested Collaborative Learning for long-tailed visual recognition,
PR(147), 2024, pp. 110064.
Elsevier DOI 2312
BibRef
Earlier: A2, A1, A4, A5, A6, Only:
Nested Collaborative Learning for Long-Tailed Visual Recognition,
CVPR22(6939-6948)
IEEE DOI 2210
Long-tailed visual recognition, Collaborative learning, Online distillation, Deep learning. Training, Representation learning, Visualization, Uncertainty, Codes, Supervised learning, Transfer/low-shot/long-tail learning, retrieval BibRef

Schultz, K.[Kristian], Bej, S.[Saptarshi], Hahn, W.[Waldemar], Wolfien, M.[Markus], Srivastava, P.[Prashant], Wolkenhauer, O.[Olaf],
ConvGeN: A convex space learning approach for deep-generative oversampling and imbalanced classification of small tabular datasets,
PR(147), 2024, pp. 110138.
Elsevier DOI 2312
Imbalanced data, Convex space learning, LoRAS, GAN, Tabular data BibRef

Baik, J.S.[Jae Soon], Yoon, I.Y.[In Young], Choi, J.W.[Jun Won],
DBN-Mix: Training dual branch network using bilateral mixup augmentation for long-tailed visual recognition,
PR(147), 2024, pp. 110107.
Elsevier DOI 2312
Long-tailed visual recognition, Class imbalance, Image classification, Mixup augmentation, Temperature scaling BibRef

Jabbari, H.[Hamed], Bigdeli, N.[Nooshin],
A new hierarchical algorithm based on CapsGAN for imbalanced image classification,
IET-IPR(18), No. 1, 2024, pp. 194-210.
DOI Link 2401
capsule network, data augmentation, deep Learning, generative adversarial networks, imbalanced image classification BibRef

Du, Y.J.[Ying-Jun], Sun, H.L.[Hao-Liang], Zhen, X.T.[Xian-Tong], Xu, J.[Jun], Yin, Y.L.[Yi-Long], Shao, L.[Ling], Snoek, C.G.M.[Cees G. M.],
MetaKernel: Learning Variational Random Features With Limited Labels,
PAMI(46), No. 3, March 2024, pp. 1464-1478.
IEEE DOI 2402
Task analysis, Kernel, Adaptation models, Prototypes, Optimization, Neural networks, Memory modules, Meta learning, few-shot learning, random features BibRef

Du, Y.J.[Ying-Jun], Shen, J.Y.[Jia-Yi], Zhen, X.T.[Xian-Tong], Snoek, C.G.M.[Cees G. M.],
SuperDisco: Super-Class Discovery Improves Visual Recognition for the Long-Tail,
CVPR23(19944-19954)
IEEE DOI 2309
BibRef

Xu, Z.Z.[Zheng-Zhuo], Chai, Z.[Zenghao], Xu, C.[Chengyin], Yuan, C.[Chun], Yang, H.Q.[Hai-Qin],
Towards Effective Collaborative Learning in Long-Tailed Recognition,
MultMed(26), 2024, pp. 3754-3764.
IEEE DOI 2402
Tail, Federated learning, Task analysis, Uncertainty, Training, Head, Feature extraction, Image classification, long tail recognition, knowledge distillation BibRef

Farhadpour, S.[Sarah], Warner, T.A.[Timothy A.], Maxwell, A.E.[Aaron E.],
Selecting and Interpreting Multiclass Loss and Accuracy Assessment Metrics for Classifications with Class Imbalance: Guidance and Best Practices,
RS(16), No. 3, 2024, pp. 533.
DOI Link 2402
BibRef

Ma, Y.[Yanbiao], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Yang, S.Y.[Shu-Yuan], Liu, X.[Xu], Chen, P.[Puhua],
Feature Distribution Representation Learning Based on Knowledge Transfer for Long-Tailed Classification,
MultMed(26), 2024, pp. 2772-2784.
IEEE DOI 2402
Tail, Training, Head, Feature extraction, Knowledge transfer, Representation learning, Noise measurement, knowledge transfer BibRef

Elbatel, M.[Marawan], Martí, R.[Robert], Li, X.M.[Xiao-Meng],
FoPro-KD: Fourier Prompted Effective Knowledge Distillation for Long-Tailed Medical Image Recognition,
MedImg(43), No. 3, March 2024, pp. 954-965.
IEEE DOI Code:
WWW Link. 2403
Biomedical imaging, Adaptation models, Task analysis, Tuning, Data models, Transformers, Image classification, Visual prompting, long tailed learning BibRef

Wang, W.Q.[Wei-Qiu], Chen, Z.[Zining], Su, F.[Fei], Zhao, Z.C.[Zhi-Cheng],
Text-guided Fourier Augmentation for long-tailed recognition,
PRL(179), 2024, pp. 38-44.
Elsevier DOI 2403
Long-tailed visual recognition, Language models, Fourier transform, Imbalanced data BibRef

Chen, J.H.[Jia-Hao], Su, B.[Bing],
Instance-Specific Semantic Augmentation for Long-Tailed Image Classification,
IP(33), 2024, pp. 2544-2557.
IEEE DOI 2404
Tail, Semantics, Head, Programmable logic arrays, Training, Image classification, Reliability, Long-tailed distribution, imbalanced data BibRef

Zhang, S.Y.[Shao-Yu], Chen, C.[Chen], Xie, Q.[Qiong], Sun, H.G.[Hai-Gang], Dong, F.[Fei], Peng, S.[Silong],
Distribution Unified and Probability Space Aligned Teacher-Student Learning for Imbalanced Visual Recognition,
CirSysVideo(34), No. 4, April 2024, pp. 2414-2425.
IEEE DOI 2404
Training, Predictive models, Smoothing methods, Data models, Visualization, Training data, Sun, Class-imbalanced learning, teacher-student learning BibRef

Liu, H.F.[Hua-Feng], Sheng, M.M.[Meng-Meng], Sun, Z.[Zeren], Yao, Y.Z.[Ya-Zhou], Hua, X.S.[Xian-Sheng], Shen, H.T.[Heng-Tao],
Learning With Imbalanced Noisy Data by Preventing Bias in Sample Selection,
MultMed(26), 2024, pp. 7426-7437.
IEEE DOI 2405
Noise measurement, Training, Tail, Predictive models, Data models, Sun, Self-supervised learning, Imbalanced label noise, average confidence margin BibRef

Guo, X.Y.[Xiao-Yu], Wei, X.[Xiang], Zhang, S.[Shunli], Lu, W.[Wei], Xing, W.W.[Wei-Wei],
DCRP: Class-Aware Feature Diffusion Constraint and Reliable Pseudo-Labeling for Imbalanced Semi-Supervised Learning,
MultMed(26), 2024, pp. 7146-7159.
IEEE DOI 2405
Training, Feature extraction, Semisupervised learning, Reliability, Data models, Data augmentation, Tail, Class-imbalanced learning, semi-supervised learning BibRef


Ye, C.[Changkun], Tsuchida, R.[Russell], Petersson, L.[Lars], Barnes, N.M.[Nick M.],
Label Shift Estimation for Class-Imbalance Problem: A Bayesian Approach,
WACV24(1062-1071)
IEEE DOI Code:
WWW Link. 2404
Adaptation models, Monte Carlo methods, Codes, Computational modeling, Estimation, Data models, Algorithms, Image recognition and understanding BibRef

Kalla, J.[Jayateja], Biswas, S.[Soma],
Robust Feature Learning and Global Variance-Driven Classifier Alignment for Long-Tail Class Incremental Learning,
WACV24(32-41)
IEEE DOI Code:
WWW Link. 2404
Representation learning, Power measurement, Codes, Prototypes, Tail, Data models, Algorithms, Machine learning architectures, Image recognition and understanding BibRef

Zhang, S.[Shan], Ni, Y.[Yao], Du, J.[Jinhao], Liu, Y.X.[Yan-Xia], Koniusz, P.[Piotr],
Semantic Transfer from Head to Tail: Enlarging Tail Margin for Long-Tailed Visual Recognition,
WACV24(1339-1349)
IEEE DOI 2404
Training, Visualization, Head, Semantics, Tail, Benchmark testing, Fasteners, Algorithms, Image recognition and understanding, Virtual / augmented reality BibRef

Dixit, A.[Abhishek], Mani, A.[Ashish],
GeometricSMOTE-Enhanced Deep Gaussian Mixture Models for Imbalanced Data Classification,
ICCVMI23(1-6)
IEEE DOI 2403
Deep learning, Training, Analytical models, Data analysis, Merging, Benchmark testing, Probabilistic logic, SMOTE, Class Imbalance, Imbalance learning BibRef

Zhao, Y.[Yu], Wang, N.[Nan],
Parameter selection of Gaussian kernel for cost-sensitive support vector machines in imbalanced data classification,
CVIDL23(243-249)
IEEE DOI 2403
Support vector machines, Deep learning, Classification algorithms, Behavioral sciences, Indexes, Kernel, Recall BibRef

Zhou, Y.X.[Yi-Xuan], Qu, Y.[Yi], Xu, X.[Xing], Shen, H.T.[Heng-Tao],
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition,
ICCV23(11311-11321)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lu, Y.[Yang], Zhang, Y.L.[Yi-Liang], Han, B.[Bo], Cheung, Y.M.[Yiu-Ming], Wang, H.Z.[Han-Zi],
Label-Noise Learning with Intrinsically Long-Tailed Data,
ICCV23(1369-1378)
IEEE DOI Code:
WWW Link. 2401
BibRef

Dong, N.[Na], Zhang, Y.Q.[Yong-Qiang], Ding, M.L.[Ming-Li], Lee, G.H.[Gim Hee],
Boosting Long-tailed Object Detection via Step-wise Learning on Smooth-tail Data,
ICCV23(6917-6926)
IEEE DOI Code:
WWW Link. 2401
BibRef

Tao, Y.[Yingfan], Sun, J.[Jingna], Yang, H.[Hao], Chen, L.[Li], Wang, X.[Xu], Yang, W.M.[Wen-Ming], Du, D.[Daniel], Zheng, M.[Min],
Local and Global Logit Adjustments for Long-Tailed Learning,
ICCV23(11749-11758)
IEEE DOI 2401
BibRef

Zhang, S.[Shaoyu], Chen, C.[Chen], Peng, S.[Silong],
Reconciling Object-Level and Global-Level Objectives for Long-Tail Detection,
ICCV23(18936-18946)
IEEE DOI Code:
WWW Link. 2401
BibRef

Chen, X.H.[Xiao-Hua], Zhou, Y.[Yucan], Wu, D.[Dayan], Yang, C.[Chule], Li, B.[Bo], Hu, Q.H.[Qing-Hua], Wang, W.P.[Wei-Ping],
AREA: Adaptive Reweighting via Effective Area for Long-Tailed Classification,
ICCV23(19220-19230)
IEEE DOI Code:
WWW Link. 2401
BibRef

Park, M.H.[Min-Ho], Kim, H.I.[Hyung-Il], Song, H.J.[Hwa Jeon], Kang, D.O.[Dong-Oh],
Augmenting Features via Contrastive Learning-based Generative Model for Long-Tailed Classification,
LIMIT23(1010-1019)
IEEE DOI 2401
BibRef

Zhao, Q.H.[Qi-Hao], Jiang, C.[Chen], Hu, W.[Wei], Zhang, F.[Fan], Liu, J.[Jun],
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed Recognition,
ICCV23(11563-11574)
IEEE DOI Code:
WWW Link. 2401
BibRef

Lin, C.S.[Ci-Siang], Chen, M.H.[Min-Hung], Wang, Y.C.A.F.[Yu-Chi-Ang Frank],
Frequency-Aware Self-Supervised Long-Tailed Learning,
LIMIT23(963-972)
IEEE DOI 2401
BibRef

Park, W.[Wongi], Park, I.[Inhyuk], Kim, S.[Sungeun], Ryu, J.B.[Jong-Bin],
Robust Asymmetric Loss for Multi-Label Long-Tailed Learning,
CVAMD23(2703-2712)
IEEE DOI Code:
WWW Link. 2401
BibRef

Yamagishi, Y.[Yosuke], Hanaoka, S.[Shohei],
Effect of Stage Training for Long-Tailed Multi-Label Image Classification,
CVAMD23(2713-2720)
IEEE DOI 2401
BibRef

Zhang, W.Q.[Wen-Qiao], Liu, C.[Changshuo], Zeng, L.Z.[Ling-Ze], Ooi, B.[Bengchin], Tang, S.L.[Si-Liang], Zhuang, Y.T.[Yue-Ting],
Learning in Imperfect Environment: Multi-Label Classification with Long-Tailed Distribution and Partial Labels,
ICCV23(1423-1432)
IEEE DOI Code:
WWW Link. 2401
BibRef

Nápoles, G.[Gonzalo], Grau, I.[Isel],
Presumably Correct Undersampling,
CIARP23(I:420-433).
Springer DOI 2312
BibRef

Nah, W.J.[Wan Jun], Ng, C.C.[Chun Chet], Lin, C.T.[Che-Tsung], Lee, Y.K.[Yeong Khang], Kew, J.L.[Jie Long], Tan, Z.Q.[Zhi Qin], Chan, C.S.[Chee Seng], Zach, C.[Christopher], Lai, S.H.[Shang-Hong],
Rethinking Long-Tailed Visual Recognition with Dynamic Probability Smoothing and Frequency Weighted Focusing,
ICIP23(435-439)
IEEE DOI Code:
WWW Link. 2312
BibRef

Mei, S.B.[Shi-Bin], Zhao, C.L.[Cheng-Long], Yuan, S.C.[Sheng-Chao], Ni, B.B.[Bing-Bing],
Exploring and Utilizing Pattern Imbalance,
CVPR23(7569-7578)
IEEE DOI 2309
BibRef

Lim, J.[Jongin], Kim, Y.[Youngdong], Kim, B.[Byungjai], Ahn, C.[Chanho], Shin, J.[Jinwoo], Yang, E.[Eunho], Han, S.[Seungju],
BiasAdv: Bias-Adversarial Augmentation for Model Debiasing,
CVPR23(3832-3841)
IEEE DOI 2309
Due to spurious correlations in the training data. BibRef

Perrett, T.[Toby], Sinha, S.[Saptarshi], Burghardt, T.[Tilo], Mirmehdi, M.[Majid], Damen, D.[Dima],
Use Your Head: Improving Long-Tail Video Recognition,
CVPR23(2415-2425)
IEEE DOI 2309
BibRef

Wei, T.[Tong], Gan, K.[Kai],
Towards Realistic Long-Tailed Semi-Supervised Learning: Consistency is All You Need,
CVPR23(3469-3478)
IEEE DOI 2309
BibRef

Gou, Y.B.[Yuan-Biao], Hu, P.[Peng], Lv, J.C.[Jian-Cheng], Zhu, H.Y.[Hong-Yuan], Peng, X.[Xi],
Rethinking Image Super Resolution from Long-Tailed Distribution Learning Perspective,
CVPR23(14327-14336)
IEEE DOI 2309
BibRef

Du, Y.X.[Ying-Xiao], Wu, J.X.[Jian-Xin],
No One Left Behind: Improving the Worst Categories in Long-Tailed Learning,
CVPR23(15804-15813)
IEEE DOI 2309
BibRef

Du, F.[Fei], Yang, P.[Peng], Jia, Q.[Qi], Nan, F.T.[Feng-Tao], Chen, X.T.[Xiao-Ting], Yang, Y.[Yun],
Global and Local Mixture Consistency Cumulative Learning for Long-tailed Visual Recognitions,
CVPR23(15814-15823)
IEEE DOI 2309
BibRef

Ma, Y.[Yanbiao], Jiao, L.C.[Li-Cheng], Liu, F.[Fang], Yang, S.Y.[Shu-Yuan], Liu, X.[Xu], Li, L.L.[Ling-Ling],
Curvature-Balanced Feature Manifold Learning for Long-Tailed Classification,
CVPR23(15824-15835)
IEEE DOI 2309
BibRef

Aimar, E.S.[Emanuel Sanchez], Jonnarth, A.[Arvi], Felsberg, M.[Michael], Kuhlmann, M.[Marco],
Balanced Product of Calibrated Experts for Long-Tailed Recognition,
CVPR23(19967-19977)
IEEE DOI 2309
BibRef

Jin, Y.[Yan], Li, M.K.[Meng-Ke], Lu, Y.[Yang], Cheung, Y.M.[Yiu-Ming], Wang, H.Z.[Han-Zi],
Long-Tailed Visual Recognition via Self-Heterogeneous Integration with Knowledge Excavation,
CVPR23(23695-23704)
IEEE DOI 2309
BibRef

Li, J.[Jian], Meng, Z.[Ziyao], Shi, D.[Daqian], Song, R.[Rui], Diao, X.L.[Xiao-Lei], Wang, J.W.[Jing-Wen], Xu, H.[Hao],
FCC: Feature Clusters Compression for Long-Tailed Visual Recognition,
CVPR23(24080-24089)
IEEE DOI 2309
BibRef

Cai, F.[Feng], Wu, K.Y.[Ke-Yu], Wang, H.P.[Hai-Peng], Wang, F.[Feng],
A Three-Stage Framework with Reliable Sample Pool for Long-Tailed Classification,
PBVS23(479-486)
IEEE DOI 2309
BibRef

Long, H.[Haixu], Zhang, X.L.[Xiao-Lin], Liu, Y.[Yanbin], Luo, Z.[Zongtai], Liu, J.B.[Jian-Bo],
Mutual Exclusive Modulator for Long-Tailed Recognition,
L3D-IVU23(4891-4900)
IEEE DOI 2309
BibRef

Chen, J.H.[Jia-Hao], Su, B.[Bing],
Transfer Knowledge from Head to Tail: Uncertainty Calibration under Long-tailed Distribution,
CVPR23(19978-19987)
IEEE DOI 2309
BibRef

Zhou, Z.P.[Zhi-Peng], Li, L.Q.[Lan-Qing], Zhao, P.L.[Pei-Lin], Heng, P.A.[Pheng-Ann], Gong, W.[Wei],
Class-Conditional Sharpness-Aware Minimization for Deep Long-Tailed Recognition,
CVPR23(3499-3509)
IEEE DOI 2309
BibRef

Fu, S.[Siming], Chu, H.P.[Huan-Peng], He, X.X.[Xiao-Xuan], Wang, H.L.[Hua-Liang], Yang, Z.Y.[Zhen-Yu], Hu, H.J.[Hao-Ji],
Meta-prototype Decoupled Training for Long-tailed Learning,
ACCV22(VI:252-268).
Springer DOI 2307
BibRef

Xu, W.C.[Wei-Chen], Cao, J.[Jian], Fu, T.H.[Tian-Hao], Yao, H.Y.[Hong-Yi], Wang, Y.[Yuan],
Boosting Dense Long-tailed Object Detection from Data-centric View,
ACCV22(III:558-574).
Springer DOI 2307
BibRef

Penarrubia, C.[Carlos], Valero-Mas, J.J.[Jose J.], Gallego, A.J.[Antonio Javier], Calvo-Zaragoza, J.[Jorge],
Addressing Class Imbalance in Multilabel Prototype Generation for k-nearest Neighbor Classification,
IbPRIA23(15-27).
Springer DOI 2307
BibRef

Jaiswal, A.[Ajay], Chen, T.L.[Tian-Long], Rousseau, J.F.[Justin F.], Peng, Y.F.[Yi-Fan], Ding, Y.[Ying], Wang, Z.Y.[Zhang-Yang],
Attend Who is Weak: Pruning-assisted Medical Image Localization under Sophisticated and Implicit Imbalances,
WACV23(4976-4985)
IEEE DOI 2302
Location awareness, Training, Pathology, Image color analysis, Neural networks, Training data, Skin, Biomedical/healthcare/medicine BibRef

Peng, H.Y.[Han-Yu], Pian, W.G.[Wei-Guo], Sun, M.M.[Ming-Ming], Li, P.[Ping],
Dynamic Re-weighting for Long-tailed Semi-supervised Learning,
WACV23(6453-6463)
IEEE DOI 2302
Training, Uncertainty, Annotations, Semisupervised learning, Task analysis, Algorithms: Machine learning architectures, visual reasoning BibRef

Park, C.[Changhwa], Yim, J.[Junho], Jun, E.[Eunji],
Mutual Learning for Long-Tailed Recognition,
WACV23(2674-2683)
IEEE DOI 2302
Training, Deep learning, Image recognition, Neural networks, Tail, Benchmark testing, Algorithms: Machine learning architectures, visual reasoning BibRef

Lazarow, J.[Justin], Sohn, K.[Kihyuk], Lee, C.Y.[Chen-Yu], Li, C.L.[Chun-Liang], Zhang, Z.Z.[Zi-Zhao], Pfister, T.[Tomas],
Unifying Distribution Alignment as a Loss for Imbalanced Semi-supervised Learning,
WACV23(5633-5642)
IEEE DOI 2302
Training, Codes, Supervised learning, Semisupervised learning, Entropy, Algorithms: Machine learning architectures, visual reasoning BibRef

Nagy, G.[George], Krishnamoorthy, M.[Mukkai],
One-Against-All Halfplane Dichotomies,
SSSPR22(183-192).
Springer DOI 2301
BibRef
And:
MeFirst ranking and multiple dichotomies: Via Linear Programming and Neural Networks,
ICPR22(550-556)
IEEE DOI 2212
Training, Sufficient conditions, Neural networks, Urban areas, Linear programming, Probabilistic logic, unbalanced classes BibRef

Ye, C.[Changkun], Barnes, N.M.[Nick M.], Petersson, L.[Lars], Tsuchida, R.[Russell],
Efficient Gaussian Process Model on Class-Imbalanced Datasets for Generalized Zero-Shot Learning,
ICPR22(2078-2085)
IEEE DOI 2212
Training, Prototypes, Training data, Gaussian processes, Artificial neural networks, Predictive models, Data models BibRef

Wei, Z.[Zhen], Zhang, L.[Li], Zhao, L.[Lei],
DSPOTE: Density-induced Selection Probability-based Oversampling TEchnique for Imbalanced Learning,
ICPR22(1-7)
IEEE DOI 2212
Filtering, Probability, Noise measurement, Task analysis BibRef

Riera, C.B.[Carlos Boned], Terrades, O.R.[Oriol Ramos],
Discriminative Neural Variational Model for Unbalanced Classification Tasks in Knowledge Graph,
ICPR22(2186-2191)
IEEE DOI 2212
Measurement, Couplings, Semantics, Ear, Benchmark testing, Data models BibRef

Zhang, Y.[Yupei], Zhou, Y.[Yaya], Liu, S.H.[Shu-Hui], Zhang, W.X.[Wen-Xin], Xiao, M.[Min], Shang, X.Q.[Xue-Qun],
WeStcoin: Weakly-Supervised Contextualized Text Classification with Imbalance and Noisy Labels,
ICPR22(2451-2457)
IEEE DOI 2212
Sensitivity, Costs, Codes, Text categorization, Bit error rate, Probability BibRef

Shao, Y.G.[Yang-Guang], Sun, Y.Y.[Ying-Ying], Guan, H.J.[Hong-Jiao],
Dual Self-Paced SMOTE for Imbalanced Data,
ICPR22(3083-3089)
IEEE DOI 2212
Training, Sensitivity, Graphical models, Pattern recognition, Distribution functions BibRef

Liu, B.[Bo], Li, H.X.[Hao-Xiang], Kang, H.[Hao], Hua, G.[Gang], Vasconcelos, N.M.[Nuno M.],
Breadcrumbs: Adversarial Class-Balanced Sampling for Long-Tailed Recognition,
ECCV22(XXIV:637-653).
Springer DOI 2211
BibRef

Tang, K.H.[Kai-Hua], Tao, M.Y.[Ming-Yuan], Qi, J.X.[Jia-Xin], Liu, Z.G.[Zhen-Guang], Zhang, H.W.[Han-Wang],
Invariant Feature Learning for Generalized Long-Tailed Classification,
ECCV22(XXIV:709-726).
Springer DOI 2211
BibRef

Hong, Y.[Yan], Zhang, J.[Jianfu], Sun, Z.Y.[Zhong-Yi], Yan, K.[Ke],
SAFA: Sample-Adaptive Feature Augmentation for Long-Tailed Image Classification,
ECCV22(XXIV:587-603).
Springer DOI 2211
BibRef

Wang, H.L.[Hua-Liang], Fu, S.M.[Si-Ming], He, X.X.[Xiao-Xuan], Fang, H.X.[Hang-Xiang], Liu, Z.Z.[Zuo-Zhu], Hu, H.J.[Hao-Ji],
Towards Calibrated Hyper-Sphere Representation via Distribution Overlap Coefficient for Long-Tailed Learning,
ECCV22(XXIV:179-196).
Springer DOI 2211
BibRef

Cho, J.H.[Jang Hyun], Krähenbühl, P.[Philipp],
Long-tail Detection with Effective Class-Margins,
ECCV22(VIII:698-714).
Springer DOI 2211
BibRef

Dam, T.[Tanmoy], Ferdaus, M.M.[Md Meftahul], Pratama, M.[Mahardhika], Anavatti, S.G.[Sreenatha G.], Jayavelu, S.[Senthilnath], Abbass, H.[Hussein],
Latent Preserving Generative Adversarial Network for Imbalance Classification,
ICIP22(3712-3716)
IEEE DOI 2211
Costs, Codes, Fault detection, Games, Generative adversarial networks, Generators, class imbalance, oversampling techniques BibRef

Escudero-Viñolo, M.[Marcos], López-Cifuentes, A.[Alejandro],
CCL: Class-Wise Curriculum Learning for Class Imbalance Problems,
ICIP22(1476-1480)
IEEE DOI 2211
Training, Codes, Computational modeling, Data models, Complexity theory, Class imbalance, Curriculum learning, Image Classification BibRef

Rangwani, H.[Harsh], Jaswani, N.[Naman], Karmali, T.[Tejan], Jampani, V.[Varun], Babu, R.V.[R. Venkatesh],
Improving GANs for Long-Tailed Data Through Group Spectral Regularization,
ECCV22(XV:426-442).
Springer DOI 2211
BibRef

Jiang, C.M.[Chiyu Max], Najibi, M.[Mahyar], Qi, C.R.[Charles R.], Zhou, Y.[Yin], Anguelov, D.[Dragomir],
Improving the Intra-class Long-Tail in 3D Detection via Rare Example Mining,
ECCV22(X:158-175).
Springer DOI 2211
BibRef

Yang, Y.Z.[Yu-Zhe], Wang, H.[Hao], Katabi, D.[Dina],
On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond,
ECCV22(XX:57-75).
Springer DOI 2211
BibRef

Gu, X.[Xiao], Guo, Y.[Yao], Li, Z.[Zeju], Qiu, J.N.[Jia-Ning], Dou, Q.[Qi], Liu, Y.X.[Yu-Xuan], Lo, B.[Benny], Yang, G.Z.[Guang-Zhong],
Tackling Long-Tailed Category Distribution Under Domain Shifts,
ECCV22(XXIII:727-743).
Springer DOI 2211
BibRef

Tian, C.Y.[Chang-Yao], Wang, W.H.[Wen-Hai], Zhu, X.Z.[Xi-Zhou], Dai, J.F.[Ji-Feng], Qiao, Y.[Yu],
VL-LTR: Learning Class-wise Visual-Linguistic Representation for Long-Tailed Visual Recognition,
ECCV22(XXV:73-91).
Springer DOI 2211
BibRef

Zhang, J.[Jie], Zhang, L.[Lei], Li, G.[Gang], Wu, C.[Chao],
Adversarial Examples for Good: Adversarial Examples Guided Imbalanced Learning,
ICIP22(136-140)
IEEE DOI 2211
Training, Machine learning, Benchmark testing, adversarial examples, long-tail data, imbalanced learning BibRef

Yi, X.Y.[Xuan-Yu], Tang, K.[Kaihua], Hua, X.S.[Xian-Sheng], Lim, J.H.[Joo-Hwee], Zhang, H.W.[Han-Wang],
Identifying Hard Noise in Long-Tailed Sample Distribution,
ECCV22(XXVI:739-756).
Springer DOI 2211
BibRef

Xu, Y.[Yue], Li, Y.L.[Yong-Lu], Li, J.F.[Jie-Feng], Lu, C.[Cewu],
Constructing Balance from Imbalance for Long-Tailed Image Recognition,
ECCV22(XX:38-56).
Springer DOI 2211
BibRef

Ahmadzadeh, A.[Azim], Angryk, R.A.[Rafal A.],
Measuring Class-Imbalance Sensitivity of Deterministic Performance Evaluation Metrics,
ICIP22(51-55)
IEEE DOI 2211
Performance evaluation, Sensitivity, Machine learning, Behavioral sciences, Task analysis, class imbalance, ROC BibRef

Zhao, Z.[Zhen], Zhou, L.P.[Lu-Ping], Duan, Y.[Yue], Wang, L.[Lei], Qi, L.[Lei], Shi, Y.H.[Ying-Huan],
DC-SSL: Addressing Mismatched Class Distribution in Semi-Supervised Learning,
CVPR22(9747-9755)
IEEE DOI 2210
Training, Degradation, Bridges, Machine learning, Semisupervised learning, Benchmark testing, Machine learning BibRef

Park, S.[Seulki], Hong, Y.[Youngkyu], Heo, B.[Byeongho], Yun, S.[Sangdoo], Choi, J.Y.[Jin Young],
The Majority Can Help the Minority: Context-rich Minority Oversampling for Long-tailed Classification,
CVPR22(6877-6886)
IEEE DOI 2210
Codes, Benchmark testing, Pattern recognition, Classification algorithms, retrieval BibRef

Alshammari, S.[Shaden], Wang, Y.X.[Yu-Xiong], Ramanan, D.[Deva], Kong, S.[Shu],
Long-Tailed Recognition via Weight Balancing,
CVPR22(6887-6897)
IEEE DOI 2210
Training, Art, Benchmark testing, Data models, Pattern recognition, Tuning, Transfer/low-shot/long-tail learning BibRef

Li, M.[Mengke], Cheung, Y.M.[Yiu-Ming], Lu, Y.[Yang],
Long-tailed Visual Recognition via Gaussian Clouded Logit Adjustment,
CVPR22(6919-6928)
IEEE DOI 2210
Training, Visualization, Graphical models, Perturbation methods, Neural networks, Tail, Benchmark testing, Transfer/low-shot/long-tail learning BibRef

Parisot, S.[Sarah], Esperança, P.M.[Pedro M.], McDonagh, S.[Steven], Madarasz, T.J.[Tamas J.], Yang, Y.X.[Yong-Xin], Li, Z.G.[Zhen-Guo],
Long-tail Recognition via Compositional Knowledge Transfer,
CVPR22(6929-6938)
IEEE DOI 2210
Training, Analytical models, Prototypes, Tail, Performance gain, Benchmark testing, Transfer/low-shot/long-tail learning BibRef

Long, A.[Alexander], Yin, W.[Wei], Ajanthan, T.[Thalaiyasingam], Nguyen, V.[Vu], Purkait, P.[Pulak], Garg, R.[Ravi], Blair, A.[Alan], Shen, C.H.[Chun-Hua], van den Hengel, A.J.[Anton J.],
Retrieval Augmented Classification for Long-Tail Visual Recognition,
CVPR22(6949-6959)
IEEE DOI 2210
Training, Visualization, Pipelines, Memory management, Tail, Pattern recognition, Transfer/low-shot/long-tail learning, retrieval BibRef

Li, B.[Bolian], Han, Z.[Zongbo], Li, H.[Haining], Fu, H.Z.[Hua-Zhu], Zhang, C.Q.[Chang-Qing],
Trustworthy Long-Tailed Classification,
CVPR22(6960-6969)
IEEE DOI 2210
Measurement, Ethics, Evidence theory, Estimation, Distributed databases, Tail, Machine learning, privacy and ethics in vision BibRef

Wang, T.[Tong], Zhu, Y.[Yousong], Chen, Y.Y.[Ying-Ying], Zhao, C.Y.[Chao-Yang], Yu, B.[Bin], Wang, J.Q.[Jin-Qiao], Tang, M.[Ming],
C2AM Loss: Chasing a Better Decision Boundary for Long-Tail Object Detection,
CVPR22(6970-6979)
IEEE DOI 2210
Adaptation models, Sensitivity, Object detection, Detectors, Tail, Pattern recognition, Transfer/low-shot/long-tail learning, retrieval BibRef

Oh, Y.[Youngtaek], Kim, D.J.[Dong-Jin], Kweon, I.S.[In So],
DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning,
CVPR22(9776-9786)
IEEE DOI 2210
Semantics, Prototypes, Semisupervised learning, Benchmark testing, Pattern recognition, Reliability, Transfer/low-shot/long-tail learning BibRef

Fan, Y.[Yue], Dai, D.X.[Deng-Xin], Kukleva, A.[Anna], Schiele, B.[Bernt],
CoSSL: Co-Learning of Representation and Classifier for Imbalanced Semi-Supervised Learning,
CVPR22(14554-14564)
IEEE DOI 2210
Couplings, Protocols, Codes, Semisupervised learning, Benchmark testing, Distance measurement, Transfer/low-shot/long-tail learning BibRef

Yu, S.[Sihao], Guo, J.F.[Jia-Feng], Zhang, R.Q.[Ru-Qing], Fan, Y.X.[Yi-Xing], Wang, Z.Z.[Zi-Zhen], Cheng, X.Q.[Xue-Qi],
A Re-Balancing Strategy for Class-Imbalanced Classification Based on Instance Difficulty,
CVPR22(70-79)
IEEE DOI 2210
Training, Machine learning algorithms, Heuristic algorithms, Machine learning, Pattern recognition, Classification algorithms, Machine learning BibRef

Singh, G.[Gursimran], Chu, L.[Lingyang], Wang, L.[Lanjun], Pei, J.[Jian], Tian, Q.[Qi], Zhang, Y.[Yong],
Mining Minority-Class Examples with Uncertainty Estimates,
MMMod22(I:258-271).
Springer DOI 2203
BibRef

Zhang, Y.K.[Yi-Kai], Wang, Q.W.[Qi-Wei], Zhan, D.C.[De-Chuan], Ye, H.J.[Han-Jia],
Learning Debiased Representations via Conditional Attribute Interpolation,
CVPR23(7599-7608)
IEEE DOI 2309
BibRef

Ye, H.J.[Han-Jia], Zhan, D.C.[De-Chuan], Chao, W.L.[Wei-Lun],
Procrustean Training for Imbalanced Deep Learning,
ICCV21(92-102)
IEEE DOI 2203
Training, Deep learning, Knowledge engineering, Neural networks, Fitting, Training data, Recognition and classification, Transfer/Low-shot/Semi/Unsupervised Learning BibRef

Park, S.[Seulki], Lim, J.[Jongin], Jeon, Y.[Younghan], Choi, J.Y.[Jin Young],
Influence-Balanced Loss for Imbalanced Visual Classification,
ICCV21(715-724)
IEEE DOI 2203
Training, Learning systems, Visualization, Codes, Benchmark testing, Data models, Recognition and classification, Vision applications and systems BibRef

Wang, Z.[Zhenyi], Duan, T.[Tiehang], Fang, L.[Le], Suo, Q.[Qiuling], Gao, M.C.[Ming-Chen],
Meta Learning on a Sequence of Imbalanced Domains with Difficulty Awareness,
ICCV21(8927-8937)
IEEE DOI 2203
Training, Machine learning algorithms, Memory management, Machine learning, Benchmark testing, Sampling methods, Recognition and classification BibRef

Kang, H.Y.[Hae-Yong], Vu, T.[Thang], Yoo, C.D.[Chang D.],
Learning Imbalanced Datasets With Maximum Margin Loss,
ICIP21(1269-1273)
IEEE DOI 2201
Training, Schedules, Image processing, Predictive models, Prediction algorithms, Data models, Maximum Margin (MM) Loss, Label-Distribution-Aware Margin(LDAM) BibRef

Okerinde, A.[Ademola], Hsu, W.[William], Theis, T.[Tom], Nafi, N.[Nasik], Shamir, L.[Lior],
eGAN: Unsupervised Approach to Class Imbalance Using Transfer Learning,
CAIP21(I:322-331).
Springer DOI 2112
BibRef

Wei, C.[Chen], Sohn, K.[Kihyuk], Mellina, C.[Clayton], Yuille, A.L.[Alan L.], Yang, F.[Fan],
CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning,
CVPR21(10852-10861)
IEEE DOI 2111
Adaptation models, Codes, Semisupervised learning, Pattern recognition BibRef

Choi, J.W.[Jong-Won], Yi, K.M.[Kwang Moo], Kim, J.[Jihoon], Choo, J.H.[Jin-Ho], Kim, B.J.[Byoung-Jip], Chang, J.[Jinyeop], Gwon, Y.J.[Young-June], Chang, H.J.[Hyung Jin],
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning,
CVPR21(6745-6754)
IEEE DOI 2111
Training, Learning systems, Estimation, Object detection, Pattern recognition, Task analysis BibRef

Wang, J.F.[Jian-Feng], Lukasiewicz, T.[Thomas], Hu, X.L.[Xiao-Lin], Cai, J.F.[Jian-Fei], Xu, Z.H.[Zheng-Hua],
RSG: A Simple but Effective Module for Learning Imbalanced Datasets,
CVPR21(3783-3792)
IEEE DOI 2111
Training, Deep learning, Codes, Generators, Pattern recognition, Convolutional neural networks BibRef

Sharma, S.[Saurabh], Yu, N.[Ning], Fritz, M.[Mario], Schiele, B.[Bernt],
Long-Tailed Recognition Using Class-Balanced Experts,
GCPR20(86-100).
Springer DOI 2110
BibRef

Duarte, K.[Kevin], Rawat, Y.[Yogesh], Shah, M.[Mubarak],
PLM: Partial Label Masking for Imbalanced Multi-label Classification,
LLID21(2733-2742)
IEEE DOI 2109
Training, Neural networks, Linear programming, Pattern recognition, Classification algorithms BibRef

He, C.[Chen], Wang, R.P.[Rui-Ping], Chen, X.L.[Xi-Lin],
A Tale of Two CILs: The Connections between Class Incremental Learning and Class Imbalanced Learning, and Beyond,
CLVision21(3554-3564)
IEEE DOI 2109
Learning systems, Collaboration, Pattern recognition BibRef

Patashnik, O.[Or], Danon, D.[Dov], Zhang, H.[Hao], Cohen-Or, D.[Daniel],
BalaGAN: Cross-Modal Image Translation Between Imbalanced Domains,
LLID21(2653-2661)
IEEE DOI 2109
Training, Image quality, Pattern recognition, Task analysis BibRef

Yu, W.P.[Wei-Ping], Yang, T.[Taojiannan], Chen, C.[Chen],
Towards Resolving the Challenge of Long-tail Distribution in UAV Images for Object Detection,
WACV21(3257-3266)
IEEE DOI 2106
Head, Image resolution, Computational modeling, Object detection, Detectors BibRef

Kim, B.[Byungju], Hong, H.G.[Hyeong Gwon], Kim, J.[Junmo],
De-biasing Neural Networks with Estimated Offset for Class Imbalanced Learning,
WACV21(1478-1486)
IEEE DOI 2106
Training, Neural networks, Training data, Benchmark testing BibRef

Tripathi, A.[Ayush], Chakraborty, R.[Rupayan], Kopparapu, S.I.K.[Sun-Il Kumar],
A Novel Adaptive Minority Oversampling Technique for Improved Classification in Data Imbalanced Scenarios,
ICPR21(10650-10657)
IEEE DOI 2105
Training, Measurement, Machine learning algorithms, Clustering algorithms, Machine learning, Partitioning algorithms, Minority class BibRef

Kocaman, V.[Veysel], Shir, O.M.[Ofer M.], Bäck, T.[Thomas],
The Unreasonable Effectiveness of the Final Batch Normalization Layer,
ISVC21(II:81-93).
Springer DOI 2112
BibRef
And:
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study,
ICPR21(10404-10411)
IEEE DOI 2105
Training, Visualization, Uncertainty, Measurement uncertainty, Transfer learning, Pipelines, Predictive models BibRef

Aggarwal, U.[Umang], Popescu, A.[Adrian], Hudelot, C.[Céline],
Minority Class Oriented Active Learning for Imbalanced Datasets,
ICPR21(9920-9927)
IEEE DOI 2105
Training, Learning systems, Image color analysis, Annotations, Transfer learning, Performance gain, Pattern recognition BibRef

Beltrán, L.V.B.[L. Viviana Beltrán], Coustaty, M.[Mickaël], Journet, N.[Nicholas], Caicedo, J.C.[Juan C.], Doucet, A.[Antoine],
Multi-Attribute Learning With Highly Imbalanced Data,
ICPR21(9219-9226)
IEEE DOI 2105
Deep learning, Location awareness, Adaptation models, Databases, Optimized production technology, Feature extraction, Data models BibRef

Sicilia, R.[Rosa], Cordelli, E.[Ermanno], Soda, P.[Paolo],
Categorizing the feature space for two-class imbalance learning,
ICPR21(6181-6188)
IEEE DOI 2105
Training, Degradation, Reliability engineering, Classification algorithms, Pattern recognition, Proposals, Indexes, Features space BibRef

Li, Y.G.[Yong-Gang], Zhou, Y.F.[Ya-Feng], Wang, Y.T.[Yong-Tao], Qin, X.R.[Xiao-Ran], Tang, Z.[Zhi],
Dual Loss for Manga Character Recognition with Imbalanced Training Data,
ICPR21(2166-2171)
IEEE DOI 2105
Training, Measurement, Adaptation models, Fitting, Training data, Benchmark testing, Data models BibRef

Zhu, H.[Hao], Yuan, Y.[Yang], Hu, G.S.[Guo-Sheng], Wu, X.[Xiang], Robertson, N.[Neil],
Imbalance Robust Softmax for Deep Embeeding Learning,
ACCV20(V:274-291).
Springer DOI 2103
BibRef

Huang, H.[He], Saito, S.[Shunta], Kikuchi, Y.[Yuta], Matsumoto, E.[Eiichi], Tang, W.[Wei], Yu, P.S.[Philip S.],
Addressing Class Imbalance in Scene Graph Parsing by Learning to Contrast and Score,
ACCV20(VI:461-477).
Springer DOI 2103
BibRef

Dutta, T.[Titir], Singh, A.[Anurag], Biswas, S.[Soma],
Adaptive Margin Diversity Regularizer for Handling Data Imbalance in Zero-Shot SBIR,
ECCV20(V:349-364).
Springer DOI 2011
BibRef

Hu, X.T.[Xin-Ting], Jiang, Y.[Yi], Tang, K.H.[Kai-Hua], Chen, J.Y.[Jing-Yuan], Miao, C.Y.[Chun-Yan], Zhang, H.W.[Han-Wang],
Learning to Segment the Tail,
CVPR20(14042-14051)
IEEE DOI 2008
Training, Head, Visualization, Task analysis, Image segmentation, Data models, Cats BibRef

Zhou, B., Cui, Q., Wei, X., Chen, Z.,
BBN: Bilateral-Branch Network With Cumulative Learning for Long-Tailed Visual Recognition,
CVPR20(9716-9725)
IEEE DOI 2008
Training, Error analysis, Feature extraction, Data models, Visualization, Benchmark testing BibRef

Zhu, L.C.[Lin-Chao], Yang, Y.[Yi],
Inflated Episodic Memory With Region Self-Attention for Long-Tailed Visual Recognition,
CVPR20(4343-4352)
IEEE DOI 2008
Visualization, Prototypes, Training, Feature extraction, Robustness, Data models, Encoding BibRef

Peng, J., Bu, X., Sun, M., Zhang, Z., Tan, T., Yan, J.,
Large-Scale Object Detection in the Wild From Imbalanced Multi-Labels,
CVPR20(9706-9715)
IEEE DOI 2008
Object detection, Training, Machine learning, Automobiles, Toy manufacturing industry, Sampling methods, Detectors BibRef

Li, Y., Wang, T., Kang, B., Tang, S., Wang, C., Li, J., Feng, J.,
Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax,
CVPR20(10988-10997)
IEEE DOI 2008
Training, Object detection, Proposals, Adaptation models, Feature extraction, Computational modeling, Detectors BibRef

Kim, J., Jeong, J., Shin, J.,
M2m: Imbalanced Classification via Major-to-Minor Translation,
CVPR20(13893-13902)
IEEE DOI 2008
Training, Machine-to-machine communications, Neural networks, Standards, Testing, Art BibRef

Aggarwal, U., Popescu, A., Hudelot, C.,
Active Learning for Imbalanced Datasets,
WACV20(1417-1426)
IEEE DOI 2006
Labeling, Machine learning, Manuals, Uncertainty, Predictive models, Entropy, Adaptation models BibRef

Wang, T., Zhao, J., Yatskar, M., Chang, K., Ordonez, V.,
Balanced Datasets Are Not Enough: Estimating and Mitigating Gender Bias in Deep Image Representations,
ICCV19(5309-5318)
IEEE DOI 2004
gender issues, image representation, learning (artificial intelligence), neural nets, Neural networks BibRef

Wang, Y., Gan, W., Yang, J., Wu, W., Yan, J.,
Dynamic Curriculum Learning for Imbalanced Data Classification,
ICCV19(5016-5025)
IEEE DOI 2004
learning (artificial intelligence), pattern classification, Data models BibRef

Hayat, M., Khan, S., Zamir, S.W., Shen, J., Shao, L.,
Gaussian Affinity for Max-Margin Class Imbalanced Learning,
ICCV19(6468-6478)
IEEE DOI 2004
feature extraction, Gaussian processes, image classification, learning (artificial intelligence), pattern clustering, Neural networks BibRef

Khan, S.[Salman], Hayat, M.[Munawar], Zamir, S.W.[Syed Waqas], Shen, J.B.[Jian-Bing], Shao, L.[Ling],
Striking the Right Balance With Uncertainty,
CVPR19(103-112).
IEEE DOI 2002
BibRef

Kim, B.[Byungju], Kim, H.W.[Hyun-Woo], Kim, K.[Kyungsu], Kim, S.[Sungjin], Kim, J.[Junmo],
Learning Not to Learn: Training Deep Neural Networks With Biased Data,
CVPR19(9004-9012).
IEEE DOI 2002
BibRef

Anantrasirichai, N., Bull, D.,
Defectnet: Multi-Class Fault Detection on Highly-Imbalanced Datasets,
ICIP19(2481-2485)
IEEE DOI 1910
convolutional neural network, segmentation, detection, classification BibRef

Langenkämper, D.[Daniel], van Kevelaer, R.[Robin], Nattkemper, T.W.[Tim W.],
Strategies for Tackling the Class Imbalance Problem in Marine Image Classification,
CVAUI18(26-36).
Springer DOI 1901
BibRef

Liang, P., Yuan, X., Li, W., Hu, J.,
A Segmented Local Offset Method for Imbalanced Data Classification Using Quasi-Linear Support Vector Machine,
ICPR18(746-751)
IEEE DOI 1812
Support vector machines, Partitioning algorithms, Classification algorithms, Complexity theory, Training data BibRef

Sarafianos, N.[Nikolaos], Xu, X.[Xiang], Kakadiaris, I.A.[Ioannis A.],
Deep Imbalanced Attribute Classification Using Visual Attention Aggregation,
ECCV18(XI: 708-725).
Springer DOI 1810
BibRef

Nguyen, T.T.T., Liew, A.W.C., Nguyen, T.T., Wang, S.,
A Novel Bayesian Framework for Online Imbalanced Learning,
DICTA17(1-7)
IEEE DOI 1804
Bayes methods, data handling, geometry, learning (artificial intelligence), matrix algebra, Training BibRef

Sze-To, A.[Antonio], Wong, A.K.C.[Andrew K. C.],
A Weight-Selection Strategy on Training Deep Neural Networks for Imbalanced Classification,
ICIAR17(3-10).
Springer DOI 1706
BibRef

Soleymani, R., Granger, E., Fumera, G.,
Loss factors for learning Boosting ensembles from imbalanced data,
ICPR16(204-209)
IEEE DOI 1705
Boosting, Error analysis, Measurement, Pattern recognition, Standards, Training BibRef

Guan, H.J.[Hong-Jiao], Zhang, Y.T.[Ying-Tao], Xian, M.[Min], Cheng, H.D., Tang, X.L.[Xiang-Long],
WENN for individualized cleaning in imbalanced data,
ICPR16(456-461)
IEEE DOI 1705
Cleaning, Noise measurement, Robustness, Sensitivity, Shape, Training, WENN, data cleaning, imbalanced, data BibRef

Tax, D.M.J., Wang, F.,
Class-dependent, non-convex losses to optimize precision,
ICPR16(3314-3319)
IEEE DOI 1705
Labeling, Logistics, Neural networks, Optimization, Robustness, Standards, Training, Imbalanced classes, Multiple Instance Learning, Positive and Unlabeled data, Supervised learning, non-convex, optimization BibRef

Huang, C., Li, Y., Loy, C.C., Tang, X.,
Learning Deep Representation for Imbalanced Classification,
CVPR16(5375-5384)
IEEE DOI 1612
BibRef

Rong, T., Tian, X., Ng, W.W.Y.,
Location bagging-based undersampling for imbalanced classification problems,
ICWAPR16(72-77)
IEEE DOI 1611
Pattern recognition BibRef

Alejo, R.[Roberto], Monroy-de-Jesús, J.[Juan], Pacheco-Sánchez, J.H.[J. Horacio], Valdovinos, R.M.[Rosa María], Antonio-Velázquez, J.A.[Juan A.], Marcial-Romero, J.R.[J. Raymundo],
Analysing the Safe, Average and Border Samples on Two-Class Imbalance Problems in the Back-Propagation Domain,
CIARP15(699-707).
Springer DOI 1511
BibRef

Mera, C.[Carlos], Arrieta, J.[Jose], Orozco-Alzate, M.[Mauricio], Branch, J.[John],
A Bag Oversampling Approach for Class Imbalance in Multiple Instance Learning,
CIARP15(724-731).
Springer DOI 1511
BibRef

Mera, C.[Carlos], Orozco-Alzate, M.[Mauricio], Branch, J.[John],
Improving Representation of the Positive Class in Imbalanced Multiple-Instance Learning,
ICIAR14(I: 266-273).
Springer DOI 1410
BibRef

García, V., Sánchez, J.S., Ochoa-Domínguez, H.J., Cleofas-Sánchez, L.,
Dissimilarity-Based Learning from Imbalanced Data with Small Disjuncts and Noise,
IbPRIA15(370-378).
Springer DOI 1506
BibRef

Famili, A.F.[A. Fazel],
Searching for Patterns in Imbalanced Data,
CIARP14(159-166).
Springer DOI 1411
BibRef

Kockentiedt, S.[Stephen], Tönnies, K.[Klaus], Gierke, E.[Erhardt],
Predicting the Influence of Additional Training Data on Classification Performance for Imbalanced Data,
GCPR14(377-387).
Springer DOI 1411
BibRef

Sandhan, T.[Tushar], Choi, J.Y.[Jin Young],
Handling Imbalanced Datasets by Partially Guided Hybrid Sampling for Pattern Recognition,
ICPR14(1449-1453)
IEEE DOI 1412
Databases BibRef

Giraldo-Forero, A.F.[Andrés Felipe], Jaramillo-Garzón, J.A.[Jorge Alberto], Ruiz-Muñoz, J.F.[José Francisco],
Managing Imbalanced Data Sets in Multi-label Problems: A Case Study with the SMOTE Algorithm,
CIARP13(I:334-342).
Springer DOI 1311
BibRef

Hernandez, J.[Julio], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José Francisco],
An Empirical Study of Oversampling and Undersampling for Instance Selection Methods on Imbalance Datasets,
CIARP13(I:262-269).
Springer DOI 1311

See also New Method for Skeleton Pruning, A.
See also Prototype Selection for Graph Embedding Using Instance Selection. BibRef

Song, Y., Morency, L.P., Davis, R.,
Distribution-sensitive learning for imbalanced datasets,
FG13(1-6)
IEEE DOI 1309
data analysis. Datasets imbalanced across classes (faces, gestures) BibRef

Alejo, R., Toribio, P., Valdovinos, R.M., Pacheco-Sanchez, J.H.,
A Modified Back-Propagation Algorithm to Deal with Severe Two-Class Imbalance Problems on Neural Networks,
MCPR12(265-272).
Springer DOI 1208
BibRef

González-Barcenas, V.M., Rendón, E., Alejo, R., Granda-Gutiérrez, E.E., Valdovinos, R.M.,
Addressing the Big Data Multi-class Imbalance Problem with Oversampling and Deep Learning Neural Networks,
IbPRIA19(I:216-224).
Springer DOI 1910
BibRef

Alejo, R., Martínez Sotoca, J.[José], Casañ, G.A.,
An Empirical Study for the Multi-class Imbalance Problem with Neural Networks,
CIARP08(479-486).
Springer DOI 0809
BibRef

Utasi, A.[Akos],
Weighted conditional mutual information based boosting for classification of imbalanced datasets,
ICPR12(2711-2714).
WWW Link. 1302
BibRef

d'Ambrosio, R.[Roberto], Iannello, G.[Giulio], Soda, P.[Paolo],
A One-per-Class reconstruction rule for class imbalance learning,
ICPR12(1310-1313).
WWW Link. 1302
BibRef

d'Ambrosio, R.[Roberto], Soda, P.[Paolo],
Polichotomies on Imbalanced Domains by One-per-Class Compensated Reconstruction Rule,
SSSPR12(301-309).
Springer DOI 1211
One of more classes underrepresented in training. BibRef

Millán-Giraldo, M.[Mónica], García, V.[Vicente], Sánchez, J.S.[J. Salvador],
One-Sided Prototype Selection on Class Imbalanced Dissimilarity Matrices,
SSSPR12(391-399).
Springer DOI 1211
BibRef

García, V.[Vicente], Sánchez, J.S.[Javier Salvador], Mollineda, R.A.[Ramón A.],
Classification of High Dimensional and Imbalanced Hyperspectral Imagery Data,
IbPRIA11(644-651).
Springer DOI 1106
BibRef
Earlier: A1, A3, A2:
Theoretical Analysis of a Performance Measure for Imbalanced Data,
ICPR10(617-620).
IEEE DOI 1008
BibRef
Earlier: A1, A3, A2:
Index of Balanced Accuracy: A Performance Measure for Skewed Class Distributions,
IbPRIA09(441-448).
Springer DOI 0906
BibRef
And: A1, A3, A2:
A New Performance Evaluation Method for Two-Class Imbalanced Problems,
SSPR08(917-925).
Springer DOI 0812
BibRef
Earlier: A1, A2, A3:
An Empirical Study of the Behavior of Classifiers on Imbalanced and Overlapped Data Sets,
CIARP07(397-406).
Springer DOI 0711
BibRef

García, V., Mollineda, R.A., Sánchez, J.S., Alejo, R., Martínez Sotoca, J.[José],
When Overlapping Unexpectedly Alters the Class Imbalance Effects,
IbPRIA07(II: 499-506).
Springer DOI 0706
BibRef

Ghanem, A.S.[Amal S.], Venkatesh, S.[Svetha], West, G.A.W.[Geoff A.W.],
Multi-class Pattern Classification in Imbalanced Data,
ICPR10(2881-2884).
IEEE DOI 1008
BibRef
Earlier:
Learning in imbalanced relational data,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Yin, D.W.[Da-Wei], An, C.[Chang], Baird, H.S.[Henry S.],
Imbalance and Concentration in k-NN Classification,
ICPR10(2170-2173).
IEEE DOI 1008
BibRef

Nguyen, G.H.[Giang H.], Bouzerdoum, A.[Abdesselam], Phung, S.L.[Son L.],
A supervised learning approach for imbalanced data sets,
ICPR08(1-4).
IEEE DOI 0812
BibRef

Molinara, M., Ricamato, M.T., Tortorella, F.,
Facing Imbalanced Classes through Aggregation of Classifiers,
CIAP07(43-48).
IEEE DOI 0709
BibRef

Cheng, H.T.[Hsien-Ting], Chen, C.S.[Chu-Song],
A Complementary Ordering Method for Class Imbalanced Problem,
ICPR06(III: 429-432).
IEEE DOI 0609
Asymmetric Bagging with Vector Complementary Ordering. Apply to biometrics. BibRef

Cantador, I.[Iván], Dorronsoro, J.R.[José R.],
Parallel Perceptrons, Activation Margins and Imbalanced Training Set Pruning,
IbPRIA05(II:43).
Springer DOI 0509
BibRef

Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Transfer Learning from Other Tasks, Other Classes .


Last update:May 6, 2024 at 15:50:14