Sima, C.[Chao],
Dougherty, E.R.[Edward R.],
Optimal convex error estimators for classification,
PR(39), No. 9, September 2006, pp. 1763-1780.
Elsevier DOI
WWW Link.
0606
Bootstrap, Cross-validation, Error estimation;
Feature-set ranking, Optimal estimation, Resubstitution,
BibRef
Wei, H.L.[Hua-Liang],
Billings, S.A.,
Feature Subset Selection and Ranking for Data Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 162-166.
IEEE DOI
0701
Forward Orthogonal Search. Select features 1 at a time.
BibRef
Liang, J.N.[Jian-Ning],
Yang, S.[Su],
Winstanley, A.[Adam],
Invariant optimal feature selection:
A distance discriminant and feature ranking based solution,
PR(41), No. 5, May 2008, pp. 1429-1439.
Elsevier DOI
0711
Optimal feature selection, Distance discriminant, Feature ranking
BibRef
Yang, S.[Su],
Liang, J.N.[Jian-Ning],
Wang, Y.Y.[Yuan-Yuan],
Winstanley, A.[Adam],
Feature Selection Based on Run Covering,
PSIVT06(208-217).
Springer DOI
0612
BibRef
Hong, Y.[Yi],
Kwong, S.[Sam],
Chang, Y.C.[Yu-Chou],
Ren, Q.S.[Qing-Sheng],
Consensus unsupervised feature ranking from multiple views,
PRL(29), No. 5, 1 April 2008, pp. 595-602.
Elsevier DOI
0802
Clustering, Feature ranking ensembles, Unsupervised feature selection
BibRef
Uematsu, K.,
Lee, Y.,
Statistical Optimality in Multipartite Ranking and Ordinal Regression,
PAMI(37), No. 5, May 2015, pp. 1080-1094.
IEEE DOI
1504
Measurement
BibRef
Bellal, F.[Fazia],
Elghazel, H.[Haytham],
Aussem, A.[Alex],
A semi-supervised feature ranking method with ensemble learning,
PRL(33), No. 10, 15 July 2012, pp. 1426-1433.
Elsevier DOI
1205
Semi-supervised learning, Feature selection, Ensemble learning
BibRef
Hernandez-Leal, P.[Pablo],
Carrasco-Ochoa, J.A.[J. Ariel],
Martínez-Trinidad, J.F.[José Francisco],
Olvera-Lopez, J.A.[J. Arturo],
InstanceRank based on borders for instance selection,
PR(46), No. 1, January 2013, pp. 365-375.
Elsevier DOI
1209
Instance selection, Instance ranking, Border instances, Supervised
classification
BibRef
Olvera-López, J.A.[J. Arturo],
Martínez-Trinidad, J.F.[José Francisco],
Carrasco-Ochoa, J.A.[J. Ariel],
Mixed Data Object Selection Based on Clustering and Border Objects,
CIARP07(674-683).
Springer DOI
0711
Instance selection.
BibRef
Hernandez-Rodriguez, S.[Selene],
Martínez-Trinidad, J.F.[José Francisco],
Carrasco-Ochoa, J.A.[J. Ariel],
On the selection of base prototypes for LAESA and TLAESA classifiers,
ICPR08(1-4).
IEEE DOI
0812
BibRef
Jiang, Y.G.[Yu-Gang],
Wang, J.[Jun],
Xue, X.,
Chang, S.F.[Shih-Fu],
Query-Adaptive Image Search With Hash Codes,
MultMed(15), No. 2, 2013, pp. 442-453.
IEEE DOI
1302
BibRef
Jiang, Y.G.[Yu-Gang],
Wang, J.[Jun],
Chang, S.F.[Shih-Fu],
Lost in binarization: query-adaptive ranking for similar image search
with compact codes,
ICMR11(16).
DOI Link
1301
BibRef
And: A2, A1, A3:
Label diagnosis through self tuning for web image search,
CVPR09(1390-1397).
IEEE DOI
0906
Are the initial label good?
BibRef
Cánovas-García, F.[Fulgencio],
Alonso-Sarría, F.[Francisco],
Optimal Combination of Classification Algorithms and Feature Ranking
Methods for Object-Based Classification of Submeter Resolution
Z/I-Imaging DMC Imagery,
RS(7), No. 4, 2015, pp. 4651-4677.
DOI Link
1505
BibRef
Lee, J.S.[Jae-Sung],
Kim, D.W.[Dae-Won],
Feature selection for multi-label classification using multivariate
mutual information,
PRL(34), No. 3, 1 February 2013, pp. 349-357.
Elsevier DOI
1301
Multi-label feature selection, Multivariate feature selection;
Multivariate mutual information, Label dependency
BibRef
Lee, J.S.[Jae-Sung],
Kim, D.W.[Dae-Won],
SCLS: Multi-label feature selection based on scalable criterion for
large label set,
PR(66), No. 1, 2017, pp. 342-352.
Elsevier DOI
1704
Machine learning
BibRef
Lim, H.K.[Hyun-Ki],
Kim, D.W.[Dae-Won],
Convex optimization approach for multi-label feature selection based
on mutual information,
ICPR16(1512-1517)
IEEE DOI
1705
Convex functions, Entropy, Linear programming, Mutual information,
Optimization, Redundancy, Time, complexity
BibRef
Lim, H.K.[Hyun-Ki],
Lee, J.S.[Jae-Sung],
Kim, D.W.[Dae-Won],
Accelerating Multi-Label Feature Selection Based on Low-Rank
Approximation,
IEICE(E99-D), No. 5, May 2016, pp. 1396-1399.
WWW Link.
1605
BibRef
Lim, H.K.[Hyun-Ki],
Lee, J.S.[Jae-Sung],
Kim, D.W.[Dae-Won],
Optimization approach for feature selection in multi-label
classification,
PRL(89), No. 1, 2017, pp. 25-30.
Elsevier DOI
1704
Multi-label feature selection
BibRef
Lee, J.S.[Jae-Sung],
Kim, D.W.[Dae-Won],
Fast multi-label feature selection based on information-theoretic
feature ranking,
PR(48), No. 9, 2015, pp. 2761-2771.
Elsevier DOI
1506
Multi-label feature selection
BibRef
Senawi, A.[Azlyna],
Wei, H.L.[Hua-Liang],
Billings, S.A.[Stephen A.],
A new maximum relevance-minimum multicollinearity (MRmMC) method for
feature selection and ranking,
PR(67), No. 1, 2017, pp. 47-61.
Elsevier DOI
1704
Dimensionality reduction
BibRef
Ji, Z.,
Cui, B.,
Li, H.,
Jiang, Y.,
Xiang, T.,
Hospedales, T.M.[Timothy M.],
Fu, Y.,
Deep Ranking for Image Zero-Shot Multi-Label Classification,
IP(29), 2020, pp. 6549-6560.
IEEE DOI
2007
Testing, Training, Predictive models, Semantics, Correlation,
Visualization, Training data, Multi-label classification,
transductive learning
BibRef
Chen, Z.M.[Zhao-Min],
Cui, Q.[Quan],
Wei, X.S.[Xiu-Shen],
Jin, X.[Xin],
Guo, Y.[Yanwen],
Disentangling, Embedding and Ranking Label Cues for Multi-Label Image
Recognition,
MultMed(23), 2021, pp. 1827-1840.
IEEE DOI
2107
Correlation, Image recognition, Streaming media,
Recurrent neural networks, Task analysis, Computational modeling,
ranking
BibRef
Viola, R.[Rémi],
Gautheron, L.[Léo],
Habrard, A.[Amaury],
Sebban, M.[Marc],
MetaAP: A meta-tree-based ranking algorithm optimizing the average
precision from imbalanced data,
PRL(161), 2022, pp. 161-167.
Elsevier DOI
2209
Imbalanced learning, Tree-based ranking, Average precision, Interpretability
BibRef
Fu, Z.[Zheren],
Mao, Z.D.[Zhen-Dong],
Yan, C.G.[Cheng-Gang],
Liu, A.A.[An-An],
Xie, H.T.[Hong-Tao],
Zhang, Y.D.[Yong-Dong],
Self-Supervised Synthesis Ranking for Deep Metric Learning,
CirSysVideo(32), No. 7, July 2022, pp. 4736-4750.
IEEE DOI
2207
Measurement, Semantics, Transforms, Training, Task analysis,
Coordinate measuring machines, Manifolds, Deep metric learning,
generative model
BibRef
Geng, X.[Xin],
Zheng, R.Y.[Ren-Yi],
Lv, J.Q.[Jia-Qi],
Zhang, Y.[Yu],
Multilabel Ranking with Inconsistent Rankers,
PAMI(44), No. 9, September 2022, pp. 5211-5224.
IEEE DOI
2208
Training, Predictive models, Adaptation models, Task analysis,
Machine learning, Machine learning algorithms, Encoding
BibRef
Geng, X.[Xin],
Luo, L.[Longrun],
Multilabel Ranking with Inconsistent Rankers,
CVPR14(3742-3747)
IEEE DOI
1409
BibRef
Helm, H.S.[Hayden S.],
Basu, A.[Amitabh],
Athreya, A.[Avanti],
Park, Y.[Youngser],
Vogelstein, J.T.[Joshua T.],
Priebe, C.E.[Carey E.],
Winding, M.[Michael],
Zlatic, M.[Marta],
Cardona, A.[Albert],
Bourke, P.[Patrick],
Larson, J.[Jonathan],
Abdin, M.[Marah],
Choudhury, P.[Piali],
Yang, W.W.[Wei-Wei],
White, C.W.[Christopher W.],
Distance-based positive and unlabeled learning for ranking,
PR(134), 2023, pp. 109085.
Elsevier DOI
2212
Positive-and-unlabeled learning, ranking, network analysis
BibRef
Fu, Y.Q.[Yu-Qian],
Fu, Y.W.[Yan-Wei],
Chen, J.J.[Jing-Jing],
Jiang, Y.G.[Yu-Gang],
Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by
Labeled Target Data,
IP(31), 2022, pp. 7078-7090.
IEEE DOI
2212
Feature extraction, Task analysis, Training, Data models,
Data mining, Benchmark testing, Visualization,
contrastive learning
BibRef
Xu, C.M.[Cheng-Ming],
Fu, Y.W.[Yan-Wei],
Liu, C.[Chen],
Wang, C.J.[Cheng-Jie],
Li, J.L.[Ji-Lin],
Huang, F.Y.[Fei-Yue],
Zhang, L.[Li],
Xue, X.Y.[Xiang-Yang],
Learning Dynamic Alignment via Meta-filter for Few-shot Learning,
CVPR21(5178-5187)
IEEE DOI
2111
Visualization, Adaptation models, Semantics,
Benchmark testing, Ordinary differential equations, Information filters
BibRef
Li, P.[Pan],
Gong, S.G.[Shao-Gang],
Wang, C.J.[Cheng-Jie],
Fu, Y.W.[Yan-Wei],
Ranking Distance Calibration for Cross-Domain Few-Shot Learning,
CVPR22(9089-9098)
IEEE DOI
2210
Training, Image retrieval, Encoding, Calibration,
Pattern recognition, Task analysis, Representation learning
BibRef
Sun, X.X.[Xiao-Xiao],
Hou, Y.Z.[Yun-Zhong],
Deng, W.J.[Wei-Jian],
Li, H.D.[Hong-Dong],
Zheng, L.[Liang],
Ranking Models in Unlabeled New Environments,
ICCV21(11741-11751)
IEEE DOI
2203
Measurement, Codes, Annotations, Computational modeling,
Search problems, Task analysis, Image and video retrieval,
Datasets and evaluation
BibRef
Li, Y.D.[Yan-Dong],
Jia, X.[Xuhui],
Sang, R.X.[Ruo-Xin],
Zhu, Y.K.[Yu-Kun],
Green, B.[Bradley],
Wang, L.Q.[Li-Qiang],
Gong, B.Q.[Bo-Qing],
Ranking Neural Checkpoints,
CVPR21(2662-2672)
IEEE DOI
2111
To use in transfer learning.
Deep learning, Training, Network topology, Transfer learning,
Benchmark testing, Feature extraction, Topology
BibRef
Vargas-Ruíz, L.[Lauro],
Franco-Arcega, A.[Anilu],
Alonso-Lavernia, M.[María_de_los_Ángeles],
A Novel Criterion to Obtain the Best Feature Subset from Filter Ranking
Methods,
MCPR18(12-22).
Springer DOI
1807
BibRef
Li, Y.,
Song, Y.,
Luo, J.,
Improving Pairwise Ranking for Multi-label Image Classification,
CVPR17(1837-1845)
IEEE DOI
1711
Adaptation models, Fasteners, Neural networks, Visualization
BibRef
Yao, Y.,
Xin, X.,
Guo, P.,
A rank minimization-based late fusion method for multi-label image
annotation,
ICPR16(847-852)
IEEE DOI
1705
Matrix decomposition, Minimization, Optimization,
Predictive models, Sparse matrices, Training
BibRef
Kanehira, A.,
Harada, T.,
Multi-label Ranking from Positive and Unlabeled Data,
CVPR16(5138-5146)
IEEE DOI
1612
BibRef
Cruz, R.[Ricardo],
Fernandes, K.[Kelwin],
Pinto Costa, J.F.[Joaquim F.],
Ortiz, M.P.[María Pérez],
Cardoso, J.S.[Jaime S.],
Ordinal Class Imbalance with Ranking,
IbPRIA17(3-12).
Springer DOI
1706
BibRef
Nogueira, S.[Sarah],
Sechidis, K.[Konstantinos],
Brown, G.[Gavin],
On the Use of Spearman's Rho to Measure the Stability of Feature
Rankings,
IbPRIA17(381-391).
Springer DOI
1706
stability to training data perturbations.
BibRef
Chen, L.[Lin],
Zhang, Q.A.[Qi-Ang],
Li, B.X.[Bao-Xin],
Predicting Multiple Attributes via Relative Multi-task Learning,
CVPR14(1027-1034)
IEEE DOI
1409
learn ranking functions describing the relative strength of attributes.
BibRef
Shankar, S.[Sukrit],
Lasenby, J.[Joan],
Cipolla, R.[Roberto],
Semantic Transform:
Weakly Supervised Semantic Inference for Relating Visual Attributes,
ICCV13(361-368)
IEEE DOI
1403
Ranking attributes for classification.
Optimization, Ranking, Semantic Descriptions
BibRef
Shi, Z.Y.[Zhi-Yuan],
Siva, P.[Parthipan],
Xiang, T.[Tony],
Transfer Learning by Ranking for Weakly Supervised Object Annotation,
BMVC12(78).
DOI Link
1301
BibRef
Diamantini, C.[Claudia],
Gemelli, A.[Alberto],
Potena, D.[Domenico],
Feature Ranking Based on Decision Border,
ICPR10(609-612).
IEEE DOI
1008
BibRef
Parakhin, M.[Mikhail],
Haluptzok, P.[Patrick],
Finding the Most Probable Ranking of Objects with Probabilistic
Pairwise Preferences,
ICDAR09(616-620).
IEEE DOI
0907
Ranking when pairwise ranking is inconsistent (not transitive).
apply to handwriting.
BibRef
Bucak, S.S.[Serhat S.],
Mallapragada, P.K.[Pavan Kumar],
Jin, R.[Rong],
Jain, A.K.[Anil K.],
Efficient multi-label ranking for multi-class learning:
Application to object recognition,
ICCV09(2098-2105).
IEEE DOI
0909
Not just binary classification. Order the many possible classes.
BibRef
Merler, M.[Michele],
Yan, R.[Rong],
Smith, J.R.[John R.],
Imbalanced RankBoost for efficiently ranking large-scale image/video
collections,
CVPR09(2607-2614).
IEEE DOI
0906
BibRef
Li, Y.[Yun],
Lu, B.L.[Bao-Liang],
Wu, Z.F.[Zhong-Fu],
A Hybrid Method of Unsupervised Feature Selection Based on Ranking,
ICPR06(II: 687-690).
IEEE DOI
0609
BibRef
Zhu, X.Q.[Xing-Quan],
Wu, X.D.[Xin-Dong],
Scalable Representative Instance Selection and Ranking,
ICPR06(III: 352-355).
IEEE DOI
0609
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Probabilistic Latent Semantic Analysis, pLSA. .