14.1.3 Feature Selection in Pattern Recognition or Clustering

Chapter Contents (Back)
Feature Selection. Classification. Pattern Recognition. Deciding which features are relevant for a classification task is feature selection. This includes a lot of very similar ways.
See also Sparse Feature Selection.
See also Unsupervised Feature Selection.
See also Feature Selection using Search and Learning.
See also Ranking.

Andrews, H.C.,
Multidimensional Rotations in Feature Selection,
TC(20), No. 9, September 1971, pp. 1045. BibRef 7109

Rasek, E.,
A contribution to the problem of feature selection with similarity functionals in pattern recognition,
PR(3), No. 1, April 1971, pp. 31-36.
Elsevier DOI 0309
BibRef

Chen, C.H.,
On a class of computationally efficient feature selection criteria,
PR(7), No. 1-2, June 1975, pp. 87-94.
Elsevier DOI 0309
BibRef

Kowalski, B.R., Bender, C.F.,
An orthogonal feature selection method,
PR(8), No. 1, January 1976, pp. 1-4.
Elsevier DOI 0309
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Christensen, R., Reichert, T.,
Unit measure violations in pattern recognition: Ambiguity and irrelevancy,
PR(8), No. 4, October 1976, pp. 239-245.
Elsevier DOI 0309
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Decell, Jr., H.P., Guseman, Jr., L.F.,
Linear feature selection with applications,
PR(11), No. 1, 1979, pp. 55-63.
Elsevier DOI 0309
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Bryant, J.[Jack], Guseman, Jr., L.F.,
Distance preserving linear feature selection,
PR(11), No. 5-6, 1979, pp. 347-352.
Elsevier DOI 0309
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Boekee, D.E., van der Lubbe, J.C.A.,
Some aspects of error bounds in feature selection,
PR(11), No. 5-6, 1979, pp. 353-360.
Elsevier DOI 0309
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Peters, C.[Charles],
Feature selection for best mean square approximation of class densities,
PR(11), No. 5-6, 1979, pp. 361-364.
Elsevier DOI 0309
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Young, D.M.[Dean M.], Odell, P.L.[Patrick L.],
A formulation and comparison of two linear feature selection techniques applicable to statistical classification,
PR(17), No. 3, 1984, pp. 331-337.
Elsevier DOI 0309
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Hester, C.F., Casasent, D.,
Multivariant Techniques for Multiclass Pattern Recognition,
AppOpt(19), 1980, pp, 1758-1761. BibRef 8000

Wismath, S.K., Soong, H.P., Akl, S.G.,
Feature selection by interactive clustering,
PR(14), No. 1-6, 1981, pp. 75-80.
Elsevier DOI 0309
BibRef

di Gesù, V., Maccarone, M.C.,
Features selection and 'possibility theory',
PR(19), No. 1, 1986, pp. 63-72.
Elsevier DOI 0309
BibRef

Bidasaria, H.B.,
Least desirable feature elimination in a general pattern recognition problem,
PR(20), No. 3, 1987, pp. 365-370.
Elsevier DOI 0309
BibRef

Morgera, S.D., and Datta, L.,
Toward a Fundamental Theory of Optimal Feature Selection: Part I,
PAMI(6), No. 5, September 1984, pp. 601-616. BibRef 8409
Earlier:
Optimal Feature Selection: Part I Theory,
ICPR84(134-137). BibRef

Morgera, S.D.,
Toward a Fundamental Theory of Optimal Feature Selection: Part II -- Implementation and Computational Complexity,
PAMI(9), No. 1, January 1987, pp. 29-38. BibRef 8701

Datta, L., and Morgera, S.D.,
Optimal Feature Selection: Part II -- Implementation,
ICPR84(138-141). BibRef 8400

Malina, W.,
On an Extended Fischer Criterion for Feature Selection,
PAMI(3), 1981, pp. 611-614. BibRef 8100

Kudo, M.[Mineichi], Shimbo, M.[Masaru],
Feature selection based on the structural indices of categories,
PR(26), No. 6, June 1993, pp. 891-901.
Elsevier DOI 0401
BibRef

Siddiqui, K.J., Liu, Y.H., Hay, D.R., Suen, C.Y.,
Feature-Selection Using A Proximity-Index Optimization Model,
PRL(15), No. 11, November 1994, pp. 1137-1141. BibRef 9411

Krishnan, S., Samudravijaya, K., Rao, P.V.S.,
Feature-Selection for Pattern-Classification with Gaussian Mixture-Models: A New Objective Criterion,
PRL(17), No. 8, July 1 1996, pp. 803-809. 9608
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Thawonmas, R., Abe, S.,
A Novel-Approach to Feature-Selection Based on Analysis of Class Regions,
SMC-B(27), No. 2, April 1997, pp. 196-207.
IEEE Top Reference. 9704
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Jelonek, J., Stefanowski, J.,
Feature Subset-Selection for Classification of Histological Images,
AIMed(9), No. 3, March 1997, pp. 227-239. 9704
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Hardie, R.C., Vaidyanathan, M., McManamon, F.,
Spectral Band Selection and Classifier Design for a Multispectral Imaging Laser-Radar,
OptEng(37), No. 3, March 1998, pp. 752-762. 9804
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Holz, H.J., Loew, M.H.,
Multiclass Classifier Independent Feature Analysis,
PRL(18), No. 11-13, November 1997, pp. 1219-1224. 9806
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Cozzi, A., Worgotter, F.,
Reclustering Techniques Improve Early Vision Feature Maps,
PAA(1), No. 1, 1998, pp. 42-51. BibRef 9800

Klimesova, D., Saic, S.,
Feature Selection Algorithm and Cobweb Correlation,
PRL(19), No. 8, June 1998, pp. 681-685. 9808
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Meyer-Baese, A.[Anke], Watzel, R.[Rolf],
Transformation radial basis neural network for relevant feature selection,
PRL(19), No. 14, December 1998, pp. 1301-1306. BibRef 9812

Brunzell, H., Eriksson, J.,
Feature reduction for classification of multidimensional data,
PR(33), No. 10, October 2000, pp. 1741-1748.
Elsevier DOI 0006
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Kudo, M.[Mineichi], Sklansky, J.[Jack],
Comparison of algorithms that select features for pattern classifiers,
PR(33), No. 1, January 2000, pp. 25-41.
Elsevier DOI 0005
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Vishwanathan, S.V.N., Murty, M.N.[M. Narasimha],
Kohonen's SOM with cache,
PR(33), No. 11, November 2000, pp. 1927-1929.
Elsevier DOI 0011
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Last, M.[Mark], Kandel, A.[Abraham], Maimon, O.[Oded],
Information-theoretic algorithm for feature selection,
PRL(22), No. 6-7, May 2001, pp. 799-811.
Elsevier DOI 0105
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Yang, J.[Jian], Yang, J.Y.[Jing-Yu],
Generalized K-L transform based combined feature extraction,
PR(35), No. 1, January 2002, pp. 295-297.
Elsevier DOI 0111
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Sebban, M.[Marc], Nock, R.[Richard],
A hybrid filter/wrapper approach of feature selection using information theory,
PR(35), No. 4, April 2002, pp. 835-846.
Elsevier DOI 0201
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Devi, V.S.[V. Susheela], Murty, M.N.[M. Narasimha],
An incremental prototype set building technique,
PR(35), No. 2, February 2002, pp. 505-513.
Elsevier DOI 0201
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Swiniarski, R.W.[Roman W.], Skowron, A.[Andrzej],
Rough set methods in feature selection and recognition,
PRL(24), No. 6, March 2003, pp. 833-849.
Elsevier DOI 0301
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Skowron, A.[Andrzej], Bazan, J.[Jan], Wojnarski, M.[Marcin],
Interactive Rough-Granular Computing in Pattern Recognition,
PReMI09(92-97).
Springer DOI 0912
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Skowron, A.[Andrzej],
Discovery of Process Models from Data and Domain Knowledge: A Rough-Granular Approach,
PReMI07(192-197).
Springer DOI 0712
BibRef

Choi, E.[Euisun], Lee, C.H.[Chul-Hee],
Feature extraction based on the Bhattacharyya distance,
PR(36), No. 8, August 2003, pp. 1703-1709.
Elsevier DOI 0304
BibRef
Earlier: A2, A1:
Optimizing Feature Extraction for Multiclass Problems,
ICPR00(Vol II: 402-405).
IEEE DOI 0009
BibRef

Dong, M.[Ming], Kothari, R.[Ravi],
Feature subset selection using a new definition of classifiability,
PRL(24), No. 9-10, June 2003, pp. 1215-1225.
Elsevier DOI 0304
BibRef

Bressan, M.[Marco], Vitria, J.[Jordi],
On the selection and classification of independent features,
PAMI(25), No. 10, October 2003, pp. 1312-1317.
IEEE Abstract. 0310
Feature selection when classes are modeled by statistically independent features. BibRef

Shen, Q.A.[Qi-Ang], Jensen, R.[Richard],
Selecting informative features with fuzzy-rough sets and its application for complex systems monitoring,
PR(37), No. 7, July 2004, pp. 1351-1363.
Elsevier DOI 0405
BibRef

Valev, V.[Ventzeslav], Sankur, B.[Bulent],
Generalized Non-reducible Descriptors,
PR(37), No. 9, September 2004, pp. 1809-1815.
Elsevier DOI 0407
BibRef
Earlier: Add A2: Radeva, P.I., ICPR00(Vol II: 394-397).
IEEE DOI 0009
BibRef

Asaithambi, A.[Asai], Valev, V.[Ventzeslav],
Construction of all non-reducible descriptors,
PR(37), No. 9, September 2004, pp. 1817-1823.
Elsevier DOI 0407
BibRef

Ahmad, A.[Amir], Dey, L.[Lipika],
A feature selection technique for classificatory analysis,
PRL(26), No. 1, 1 January 2005, pp. 43-56.
Elsevier DOI 0501
BibRef

Shih, F.Y.[Frank Y.], Cheng, S.X.[Shou-Xian],
Improved feature reduction in input and feature spaces,
PR(38), No. 5, May 2005, pp. 651-659.
Elsevier DOI 0501

See also improved incremental training algorithm for support vector machines using active query, An. BibRef

Hsing, T.[Tailen], Liu, L.Y.[Li-Yu], Brun, M.[Marcel], Dougherty, E.R.[Edward R.],
The coefficient of intrinsic dependence (feature selection using el CID),
PR(38), No. 5, May 2005, pp. 623-636.
Elsevier DOI 0501
BibRef

Silva, P.J.S.[Paulo J.S.], Hashimoto, R.F.[Ronaldo F.], Kim, S.[Seungchan], Barrera, J.[Junior], Brandão, L.O.[Leônidas O.], Suh, E.[Edward], Dougherty, E.R.[Edward R.],
Feature selection algorithms to find strong genes,
PRL(26), No. 10, 15 July 2005, pp. 1444-1453.
Elsevier DOI 0506
BibRef

Peng, H.C.[Han-Chuan], Long, F.H.[Fu-Hui], Ding, C.[Chris],
Feature Selection Based on Mutual Information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy,
PAMI(27), No. 8, August 2005, pp. 1226-1238.
IEEE Abstract. 0506
BibRef

Coetzee, F.M.[Frans M.],
Correcting the Kullback-Leibler distance for feature selection,
PRL(26), No. 11, August 2005, pp. 1675-1683.
Elsevier DOI 0506
BibRef

Brown, M., Costen, N.P.,
Exploratory basis pursuit classification,
PRL(26), No. 12, September 2005, pp. 1907-1915.
Elsevier DOI 0508
BibRef
Earlier:
Non-Linear Feature Selection for Classification,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Luebke, K.[Karsten], Weihs, C.[Claus],
Improving feature extraction by replacing the Fisher criterion by an upper error bound,
PR(38), No. 11, November 2005, pp. 2220-2223.
Elsevier DOI 0509
BibRef

Sima, C.[Chao], Attoor, S.N.[Sanju N.], Braga-Neto, U.M.[Ulisses M.], Lowey, J.[James], Suh, E.[Edward], Dougherty, E.R.[Edward R.],
Impact of error estimation on feature selection,
PR(38), No. 12, December 2005, pp. 2472-2482.
Elsevier DOI 0510
BibRef

Sima, C.[Chao], Dougherty, E.R.[Edward R.],
The peaking phenomenon in the presence of feature-selection,
PRL(29), No. 11, 1 August 2008, pp. 1667-1674.
Elsevier DOI 0804
Classification, Feature-selection, Peaking phenomenon BibRef

Gasca, E., Sánchez, J.S., Alonso, R.,
Eliminating redundancy and irrelevance using a new MLP-based feature selection method,
PR(39), No. 2, February 2006, pp. 313-315.
Elsevier DOI 0512
BibRef

Nanni, L.[Loris],
Cluster-Based Pattern Discrimination: A Novel Technique for Feature Selection,
PRL(27), No. 6, 15 April 2006, pp. 682-687.
Elsevier DOI Feature evaluation and selection, Clustering, Ensemble of classifiers 0604
BibRef

Cord, A.[Aurélien], Ambroise, C.[Christophe], Cocquerez, J.P.[Jean-Pierre],
Feature selection in robust clustering based on Laplace mixture,
PRL(27), No. 6, 15 April 2006, pp. 627-635.
Elsevier DOI Clustering, Feature selection, Laplace distribution; Kruskal-Wallis statistical test, EM algorithm 0604
BibRef

Abe, N.[Naoto], Kudo, M.[Mineichi],
Non-parametric classifier-independent feature selection,
PR(39), No. 5, May 2006, pp. 737-746.
Elsevier DOI 0604
Garbage feature, Non-parametric, Two-stage feature selection Classifier-independent feature selection, Bayes classifier; BibRef

Tenmoto, H.[Hiroshi], Kudo, M.[Mineichi],
Soft Feature Selection by Using a Histogram-Based Classifier,
SSPR08(572-581).
Springer DOI 0812
BibRef

Aoki, K.[Kazuaki], Kudo, M.[Mineichi],
Feature and Classifier Selection in Class Decision Trees,
SSPR08(562-571).
Springer DOI 0812
BibRef

Lee, G.N.[Gobert N.], Bottema, M.J.[Murk J.],
Significance of classification scores subsequent to feature selection,
PRL(27), No. 14, 15 October 2006, pp. 1702-1709.
Elsevier DOI 0609
Multiple comparisons, Statistical significance, Computer-aided diagnosis BibRef

Tang, W.Y.[Wen-Yin], Mao, K.Z.,
Feature selection algorithm for mixed data with both nominal and continuous features,
PRL(28), No. 5, 1 April 2007, pp. 563-571.
Elsevier DOI 0703
Feature selection, Mixed data, Continuous feature, Nominal feature BibRef

Wang, H.Z.[Hong-Zhi], Angelopoulou, E.[Elli],
Sensor band selection for multispectral imaging via average normalized information,
RealTimeIP(1), No. 2, December 2006, pp. 109-121.
Springer DOI 0001
BibRef

Sun, Y.J.[Yi-Jun],
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications,
PAMI(29), No. 6, June 2007, pp. 1035-1051.
IEEE DOI 0704

See also Machine Learning Research: Four Current Directions. BibRef

Guyon, I.[Isabelle], Li, J.W.[Ji-Wen], Mader, T.[Theodor], Pletscher, P.A.[Patrick A.], Schneider, G.[Georg], Uhr, M.[Markus],
Competitive baseline methods set new standards for the NIPS 2003 feature selection benchmark,
PRL(28), No. 12, 1 September 2007, pp. 1438-1444.
Elsevier DOI 0707
Feature selection, Matlab, Machine learning, Classification; Challenge, Benchmark, Curriculum BibRef

Akselrod-Ballin, A.[Ayelet], Ullman, S.[Shimon],
Distinctive and compact features,
IVC(26), No. 9, 1 September 2008, pp. 1269-1276.
Elsevier DOI 0806
Feature selection, Object recognition, Distinctive features, Facial features BibRef

Feng, G.Y.[Gui-Yu], Zhang, D.[David], Yang, J.[Jian], Hu, D.[Dewen],
A Theoretical Framework For Matrix-based Feature Extraction Algorithms With Its Application To Image Recognition,
IJIG(8), No. 1, January 2008, pp. 1-23. 0801
BibRef

Ng, W.W.Y.[Wing W.Y.], Yeung, D.S.[Daniel S.], Firth, M.[Michael], Tsang, E.C.C.[Eric C.C.], Wang, X.Z.[Xi-Zhao],
Feature selection using localized generalization error for supervised classification problems using RBFNN,
PR(41), No. 12, December 2008, pp. 3706-3719.
Elsevier DOI 0810
Feature selection, Neural network, Generalization error, RBFNN BibRef

Paskaleva, B., Hayat, M.M.[Majeed M.], Wang, Z., Tyo, J.S.[J. Scott], Krishna, S.,
Canonical Correlation Feature Selection for Sensors With Overlapping Bands: Theory and Application,
GeoRS(46), No. 10, October 2008, pp. 3346-3358.
IEEE DOI 0810
Specific to issues of overlapping bands. BibRef

Parthalain, N.M.[Neil Mac], Shen, Q.A.[Qi-Ang],
Exploring the boundary region of tolerance rough sets for feature selection,
PR(42), No. 5, May 2009, pp. 655-667.
Elsevier DOI 0902
Feature selection, Attribute reduction, Rough sets, Classification BibRef

Liu, H.W.[Hua-Wen], Sun, J.G.[Ji-Gui], Liuand, L.[Lei], Zhang, H.J.[Hui-Jie],
Feature selection with dynamic mutual information,
PR(42), No. 7, July 2009, pp. 1330-1339.
Elsevier DOI 0903
Classification, Feature selection, Mutual information, Filter method BibRef

Garcia, H.C.[Hugo C.], Villalobos, J.R.[Jesus Rene], Pan, R.[Rong], Runger, G.C.[George C.],
A Novel Feature Selection Methodology for Automated Inspection Systems,
PAMI(31), No. 7, July 2009, pp. 1338-1344.
IEEE DOI 0905
Stepwise variable selection procedure using misclassification error. BibRef

Zhang, W.[Wei], Lin, Z.C.[Zhou-Chen], Tang, X.[Xiaoou],
Tensor linear Laplacian discrimination (TLLD) for feature extraction,
PR(42), No. 9, September 2009, pp. 1941-1948.
Elsevier DOI 0905
Discriminant feature extraction, Tensor, Contextual distance BibRef

Zhao, D.L.[De-Li], Lin, Z.C.[Zhou-Chen], Xiao, R.[Rong], Tang, X.[Xiaoou],
Linear Laplacian Discrimination for Feature Extraction,
CVPR07(1-7).
IEEE DOI 0706
BibRef

Hou, C.Q.[Cui-Qin], Jiao, L.C.[Li-Cheng],
Selecting features of linear-chain conditional random fields via greedy stage-wise algorithms,
PRL(31), No. 2, 15 January 2010, pp. 151-162.
Elsevier DOI 1001
Greedy stage-wise, Feature selection, Linear-chain conditional random fields, Pseudo-likelihood BibRef

Derrac, J.[Joaquín], García, S.[Salvador], Herrera, F.[Francisco],
IFS-CoCo: Instance and feature selection based on cooperative coevolution with nearest neighbor rule,
PR(43), No. 6, June 2010, pp. 2082-2105.
Elsevier DOI 1003
BibRef
Earlier:
IFS-CoCo in the Landscape Contest: Description and Results,
ICPR-Contests10(56-65).
Springer DOI 1008
Evolutionary algorithms, Feature selection, Instance selection; Cooperative coevolution, Nearest neighbor BibRef

Zhang, J.C.[Jian-Chun], Zhang, D.Q.[Dao-Qiang],
A novel ensemble construction method for multi-view data using random cross-view correlation between within-class examples,
PR(44), No. 6, June 2011, pp. 1162-1171.
Elsevier DOI 1102
Random correlation, Canonical correlation analysis, Feature extraction, Ensemble construction BibRef

Balagani, K.S.[Kiran S.], Phoha, V.V.[Vir V.], Iyengar, S.S., Balakrishnan, N.,
On Guo and Nixon's Criterion for Feature Subset Selection: Assumptions, Implications, and Alternative Options,
SMC-A(40), No. 3, May 2010, pp. 651-655.
IEEE DOI 1003
BibRef

Balagani, K.S.[Kiran S.], Phoha, V.V.[Vir V.],
On the Feature Selection Criterion Based on an Approximation of Multidimensional Mutual Information,
PAMI(32), No. 7, July 2010, pp. 1342-1343.
IEEE DOI 1006
derive the criterion from multidimensional mutual information between features and the calss. Relate to Bayes classification error. BibRef

Garcia-Borroto, M.[Milton], Martínez-Trinidad, J.F.[José Francisco], Carrasco-Ochoa, J.A.[Jesus Ariel], Medina-Perez, M.A.[Miguel Angel], Ruiz-Shulcloper, J.[Jose],
LCMine: An efficient algorithm for mining discriminative regularities and its application in supervised classification,
PR(43), No. 9, September 2010, pp. 3025-3034.
Elsevier DOI 1006
Discriminative regularities, Emerging patterns, Mixed incomplete data; Comprehensible classifiers BibRef

Medina-Pérez, M.A.[Miguel Angel], García-Borroto, M.[Milton], Ruiz-Shulcloper, J.[José],
Object Selection Based on Subclass Error Correcting for ALVOT,
CIARP07(496-505).
Springer DOI 0711
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Medina-Pérez, M.A.[Miguel Angel], García-Borroto, M.[Milton], Villuendas-Rey, Y.[Yenny], Ruiz-Shulcloper, J.[José],
Selecting Objects for ALVOT,
CIARP06(606-613).
Springer DOI 0611
Objects described by non-numeric features. BibRef

Villuendas-Rey, Y.[Yenny], García-Borroto, M.[Milton], Ruiz-Shulcloper, J.[José],
Selecting Features and Objects for Mixed and Incomplete Data,
CIARP08(381-388).
Springer DOI 0809
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Villuendas-Rey, Y.[Yenny], García-Lorenzo, M.M.[María Matilde],
Mixed Data Balancing through Compact Sets Based Instance Selection,
CIARP13(I:254-261).
Springer DOI 1311
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Villuendas-Rey, Y.[Yenny], García-Borroto, M.[Milton], Medina-Pérez, M.A.[Miguel A.], Ruiz-Shulcloper, J.[José],
Simultaneous Features and Objects Selection for Mixed and Incomplete Data,
CIARP06(597-605).
Springer DOI 0611
for the Most Similar Neighbor classifier. BibRef

Pena, J.M.[Jose M.], Nilsson, R.[Roland],
On the Complexity of Discrete Feature Selection for Optimal Classification,
PAMI(32), No. 8, August 2010, pp. 1517-1522.
IEEE DOI 1007
Only discrete features. BibRef

Somol, P.[Petr], Novovicová, J.[Jana],
Evaluating Stability and Comparing Output of Feature Selectors that Optimize Feature Subset Cardinality,
PAMI(32), No. 11, November 2010, pp. 1921-1939.
IEEE DOI 1011
BibRef
Earlier:
Evaluating the Stability of Feature Selectors That Optimize Feature Subset Cardinality,
SSPR08(956-966).
Springer DOI 0812
Robustness of methods. Evaluation of different methods. BibRef

Novovicová, J.[Jana], Somol, P.[Petr], Haindl, M.[Michal], Pudil, P.[Pavel],
Conditional Mutual Information Based Feature Selection for Classification Task,
CIARP07(417-426).
Springer DOI 0711
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Nikolaidis, K., Goulermas, J.Y., Wu, Q.H.,
A class boundary preserving algorithm for data condensation,
PR(44), No. 3, March 2011, pp. 704-715.
Elsevier DOI 1011
Machine learning, Instance based learning, Instance condensation BibRef

Nikolaidis, K., Mu, T.T., Goulermas, J.Y.[John Y.],
Prototype reduction based on Direct Weighted Pruning,
PRL(36), No. 1, 2014, pp. 22-28.
Elsevier DOI 1312
Data condensation BibRef

Mohanty, D.[Debadutta],
Covering based approximation: A new type approach,
IJCVR(1), No. 3, 2010, pp. 335-345.
DOI Link 1102
Rough set theory. BibRef

Hedjazi, L.[Lyamine], Aguilar-Martin, J.[Joseph], le Lann, M.V.[Marie-Veronique],
Similarity-margin based feature selection for symbolic interval data,
PRL(32), No. 4, 1 March 2011, pp. 578-585.
Elsevier DOI 1102
Interval data, Feature selection, Margin, Optimization, Symbolic data analysis, Classification BibRef

He, X.F.[Xiao-Fei], Ji, M.[Ming], Zhang, C.Y.[Chi-Yuan], Bao, H.J.[Hu-Jun],
A Variance Minimization Criterion to Feature Selection Using Laplacian Regularization,
PAMI(33), No. 10, October 2011, pp. 2013-2025.
IEEE DOI 1109
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Fan, M.Y.[Ming-Yu], Zhang, X.Q.[Xiao-Qin], Lin, Z.C.[Zhou-Chen], Zhang, Z.F.[Zhong-Fei], Bao, H.J.[Hu-Jun],
A Regularized Approach for Geodesic-Based Semisupervised Multimanifold Learning,
IP(23), No. 5, May 2014, pp. 2133-2147.
IEEE DOI 1405
face recognition BibRef

Wang, J.P.[Jiang-Ping], Fan, J.Y.[Jie-Yan], Li, H.H.[Huang-Huang], Wu, D.P.[Da-Peng],
Kernel-based feature extraction under maximum margin criterion,
JVCIR(23), No. 1, January 2012, pp. 53-62.
Elsevier DOI 1112
Feature extraction, Kernel method, Pattern classification, RELIEF; Maximum margin criterion, LFE, KLFE, Nonlinear transformation BibRef

Li, M.[Min], Deng, S.B.[Shao-Bo], Feng, S.Z.[Sheng-Zhong], Fan, J.P.[Jian-Ping],
An effective discretization based on Class-Attribute Coherence Maximization,
PRL(32), No. 15, 1 November 2011, pp. 1962-1973.
Elsevier DOI 1112
Discretization, CAIM, CACM, Classification, Class-Attribute Independence Redundancy (CAIR) BibRef

Sun, X.[Xin], Liu, Y.H.[Yan-Heng], Li, J.[Jin], Zhu, J.Q.[Jian-Qi], Chen, H.L.[Hui-Ling], Liu, X.J.[Xue-Jie],
Feature evaluation and selection with cooperative game theory,
PR(45), No. 8, August 2012, pp. 2992-3002.
Elsevier DOI 1204
Machine learning, Feature selection, Cooperative game theory, Filter method BibRef

Zhang, L., Chen, C.[Chun], Bu, J.J.[Jia-Jun], He, X.,
A Unified Feature and Instance Selection Framework Using Optimum Experimental Design,
IP(21), No. 5, May 2012, pp. 2379-2388.
IEEE DOI 1204
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Ekbal, A.[Asif], Saha, S.[Sriparna],
Multiobjective optimization for classifier ensemble and feature selection: an application to named entity recognition,
IJDAR(15), No. 2, June 2012, pp. 143-166.
WWW Link. 1205
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Boubezoul, A.[Abderrahmane], Paris, S.[Sébastien],
Application of global optimization methods to model and feature selection,
PR(45), No. 10, October 2012, pp. 3676-3686.
Elsevier DOI 1206
Cross-Entropy Method, Feature selection, Particle swarm optimization; Hyper-parameters optimization, Support vector machines BibRef

Deng, T.Q.[Ting-Quan], Yang, C.D.[Cheng-Dong], Wang, X.F.[Xiao-Fei],
A reduct derived from feature selection,
PRL(33), No. 12, 1 September 2012, pp. 1638-1646.
Elsevier DOI 1208
Attribute reduction, Feature selection, Decision systems, Rough sets; Data mining BibRef

Hu, W.J.[Wen-Jun], Choi, K.S.[Kup-Sze], Gu, Y.G.[Yong-Gen], Wang, S.T.[Shi-Tong],
Minimum-maximum local structure information for feature selection,
PRL(34), No. 5, 1 April 2013, pp. 527-535.
Elsevier DOI 1303
Feature selection, Laplacian Score, Locality preserving, Laplacian Eigenmap, Manifold learning BibRef

Peng, X.J.[Xin-Jun], Xu, D.[Dong],
A local information-based feature-selection algorithm for data regression,
PR(46), No. 9, September 2013, pp. 2519-2530.
Elsevier DOI 1305
Feature selection, Local information, Irrelevant feature, Least squares loss, Gradient descent, Data regression BibRef

Bennasar, M.[Mohamed], Setchi, R.[Rossitza], Hicks, Y.A.[Yulia A.],
Feature Interaction Maximisation,
PRL(34), No. 14, 2013, pp. 1630-1635.
Elsevier DOI 1308
Feature selection BibRef

Marques, J.[Joselene], Igel, C.[Christian], Lillholm, M.[Martin], Dam, E.B.[Erik B.],
Linear feature selection in texture analysis: A PLS based method,
MVA(24), No. 7, October 2013, pp. 1435-1444.
Springer DOI 1309
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Marques, J.[Joselene], Dam, E.B.[Erik B.],
Texture Analysis by a PLS Based Method for Combined Feature Extraction and Selection,
MLMI11(109-116).
Springer DOI 1109
Partial least square regression. BibRef

Bahrampour, S.[Soheil], Ray, A.[Asok], Sarkar, S.[Soumalya], Damarla, T.[Thyagaraju], Nasrabadi, N.M.[Nasser M.],
Performance comparison of feature extraction algorithms for target detection and classification,
PRL(34), No. 16, 2013, pp. 2126-2134.
Elsevier DOI 1310
Feature extraction BibRef

Lu, Z.C.[Zheng-Cai], Qin, Z.[Zheng], Zhang, Y.Q.[Yong-Qiang], Fang, J.[Jun],
A fast feature selection approach based on rough set boundary regions,
PRL(36), No. 1, 2014, pp. 81-88.
Elsevier DOI 1312
Feature selection BibRef

Zhang, D.[Di], He, J.Z.[Jia-Zhong], Zhao, Y.[Yun], Luo, Z.L.[Zhong-Liang], Du, M.H.[Ming-Hui],
Global plus local: A complete framework for feature extraction and recognition,
PR(47), No. 3, 2014, pp. 1433-1442.
Elsevier DOI 1312
Feature extraction. linear discriminant analysis. BibRef

Reif, M.[Matthias], Shafait, F.[Faisal],
Efficient feature size reduction via predictive forward selection,
PR(47), No. 4, 2014, pp. 1664-1673.
Elsevier DOI 1402
Feature selection BibRef

Gilani, S.Z.[Syed Zulqarnain], Shafait, F.[Faisal], Mian, A.[Ajmal],
Gradient based efficient feature selection,
WACV14(191-197)
IEEE DOI 1406
Accuracy BibRef

Yildiz, O.T.[Olcay Taner],
On the feature extraction in discrete space,
PR(47), No. 5, 2014, pp. 1988-1993.
Elsevier DOI 1402
Feature extraction BibRef

Cataltepe, Z.[Zehra], Sonmez, A.[Abdullah], Senliol, B.[Baris],
Feature enrichment and selection for transductive classification on networked data,
PRL(37), No. 1, 2014, pp. 41-53.
Elsevier DOI 1402
Feature enrichment BibRef

Liu, R.C.[Ruo-Chen], Chen, Y.Y.[Yang-Yang], Jiao, L.C.[Li-Cheng], Li, Y.Y.[Yang-Yang],
A particle swarm optimization based simultaneous learning framework for clustering and classification,
PR(47), No. 6, 2014, pp. 2143-2152.
Elsevier DOI 1403
Classification BibRef

Wu, B., Zhang, L., Zhao, Y.,
Feature Selection via Cramer's V-Test Discretization for Remote-Sensing Image Classification,
GeoRS(52), No. 5, May 2014, pp. 2593-2606.
IEEE DOI 1403
Association index. Applied to multiple classification techniques. BibRef

Bolón-Canedo, V., Porto-Díaz, I., Sánchez-Maroño, N., Alonso-Betanzos, A.,
A framework for cost-based feature selection,
PR(47), No. 7, 2014, pp. 2481-2489.
Elsevier DOI 1404
Cost-based feature selection BibRef

Sharma, A.[Alok], Paliwal, K.K.[Kuldip K.], Imoto, S.[Seiya], Miyano, S.[Satoru],
A feature selection method using improved regularized linear discriminant analysis,
MVA(25), No. 3, April 2014, pp. 775-786.
Springer DOI 1404
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Li, S., Wei, D.,
Extremely High-Dimensional Feature Selection via Feature Generating Samplings,
Cyber(44), No. 6, June 2014, pp. 737-747.
IEEE DOI 1406
Algorithm design and analysis BibRef

Wang, D.Q.[De-Qing], Zhang, H.[Hui], Liu, R.[Rui], Lv, W.F.[Wei-Feng], Wang, D.[Datao],
t-Test feature selection approach based on term frequency for text categorization,
PRL(45), No. 1, 2014, pp. 1-10.
Elsevier DOI 1407
Feature selection BibRef

Rivera, J.P.[Juan Pablo], Verrelst, J.[Jochem], Delegido, J.[Jesús], Veroustraete, F.[Frank], Moreno, J.[José],
On the Semi-Automatic Retrieval of Biophysical Parameters Based on Spectral Index Optimization,
RS(6), No. 6, 2014, pp. 4927-4951.
DOI Link 1407
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Qiu, J.Y.[Jun-Yang], Wang, Y.B.[Yi-Bing], Pan, Z.S.[Zhi-Song], Jia, B.[Bo],
Semi-Supervised Feature Selection with Universum Based on Linked Social Media Data,
IEICE(E97-D), No. 9, September 2014, pp. 2522-2525.
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Corrêa, G.N.[Geraldo N.], Marcacini, R.M.[Ricardo M.], Hruschka, E.R.[Eduardo R.], Rezende, S.O.[Solange O.],
Interactive textual feature selection for consensus clustering,
PRL(52), No. 1, 2015, pp. 25-31.
Elsevier DOI 1412
Interactive feature selection BibRef

Hocke, J.[Jens], Martinetz, T.[Thomas],
Maximum distance minimization for feature weighting,
PRL(52), No. 1, 2015, pp. 48-52.
Elsevier DOI 1412
Feature selection BibRef

Banka, H.[Haider], Dara, S.[Suresh],
A Hamming distance based binary particle swarm optimization (HDBPSO) algorithm for high dimensional feature selection, classification and validation,
PRL(52), No. 1, 2015, pp. 94-100.
Elsevier DOI 1412
Feature selection BibRef

Freeman, C.[Cecille], Kulic, D.[Dana], Basir, O.[Otman],
An evaluation of classifier-specific filter measure performance for feature selection,
PR(48), No. 5, 2015, pp. 1812-1826.
Elsevier DOI 1502
Feature selection BibRef

Jiang, F.[Feng], Sui, Y.F.[Yue-Fei], Zhou, L.[Lin],
A relative decision entropy-based feature selection approach,
PR(48), No. 7, 2015, pp. 2151-2163.
Elsevier DOI 1504
Rough sets BibRef

Zeng, Z.[Zilin], Zhang, H.J.[Hong-Jun], Zhang, R.[Rui], Yin, C.X.[Cheng-Xiang],
A novel feature selection method considering feature interaction,
PR(48), No. 8, 2015, pp. 2656-2666.
Elsevier DOI 1505
Feature selection BibRef

Liu, Y., Tang, F., Zeng, Z.,
Feature Selection Based on Dependency Margin,
Cyber(45), No. 6, June 2015, pp. 1209-1221.
IEEE DOI 1506
Approximation algorithms BibRef

Chen, X., Fang, T., Huo, H., Li, D.,
Measuring the Effectiveness of Various Features for Thematic Information Extraction From Very High Resolution Remote Sensing Imagery,
GeoRS(53), No. 9, September 2015, pp. 4837-4851.
IEEE DOI 1506
Accuracy BibRef

Jiang, Z.L.[Zhuo-Lin], Lin, Z.[Zhe], Ling, H.B.[Hai-Bin], Porikli, F.M.[Fatih M.], Shao, L.[Ling], Turaga, P.K.[Pavan K.],
Discriminative feature learning from big data for visual recognition,
PR(48), No. 10, 2015, pp. 2961-2963.
Elsevier DOI 1507
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Yang, Y.B.[Yu-Bin], Zhu, Q.H.[Qi-Hai], Mao, X.J.[Xiao-Jiao], Pan, L.Y.[Lin-Yan],
Visual feature coding for image classification integrating dictionary structure,
PR(48), No. 10, 2015, pp. 3067-3075.
Elsevier DOI 1507
Visual feature coding BibRef

Bouhamed, S.A.[S. Ammar], Kallel, I.K.[I. Khanfir], Masmoudi, D.S.[D. Sellami], Solaiman, B.,
Feature selection in possibilistic modeling,
PR(48), No. 11, 2015, pp. 3627-3640.
Elsevier DOI 1506
Feature selection BibRef

Huttunen, H.[Heikki], Tohka, J.[Jussi],
Model selection for linear classifiers using Bayesian error estimation,
PR(48), No. 11, 2015, pp. 3739-3748.
Elsevier DOI 1506
Logistic regression BibRef

Saha, A.[Arkajyoti], Das, S.[Swagatam],
Automated feature weighting in clustering with separable distances and inner product induced norms: A theoretical generalization,
PRL(63), No. 1, 2015, pp. 50-58.
Elsevier DOI 1508
Automated feature weights BibRef

Wong, W.K., Lai, Z., Xu, Y., Wen, J., Ho, C.P.,
Joint Tensor Feature Analysis For Visual Object Recognition,
Cyber(45), No. 11, November 2015, pp. 2425-2436.
IEEE DOI 1511
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Paul, S.[Sujoy], Das, S.[Swagatam],
Simultaneous feature selection and weighting: An evolutionary multi-objective optimization approach,
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Feature selection BibRef

Feng, G.Z.[Guo-Zhong], Guo, J.H.[Jian-Hua], Jing, B.Y.[Bing-Yi], Sun, T.[Tieli],
Feature subset selection using naive Bayes for text classification,
PRL(65), No. 1, 2015, pp. 109-115.
Elsevier DOI 1511
Bayesian model averaging BibRef

Fade, J.[Julien],
Stochastic complexity-based model selection with false alarm rate control in optical spectroscopy,
PRL(65), No. 1, 2015, pp. 152-156.
Elsevier DOI 1511
model selection BibRef

Chen, X.B.[Xiao-Bo], Cai, Y.F.[Ying-Feng], Chen, L.[Long], Li, Z.Y.[Zuo-Yong],
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Min, H.K.[Hwang-Ki], Hou, Y.X.[Yu-Xi], Park, S.[Sangwoo], Song, I.[Iickho],
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Elsevier DOI 1512
Kernel discriminant analysis BibRef

Luo, Y., Wen, Y., Tao, D., Gui, J., Xu, C.,
Large Margin Multi-Modal Multi-Task Feature Extraction for Image Classification,
IP(25), No. 1, January 2016, pp. 414-427.
IEEE DOI 1601
Correlation BibRef

Vinh, N.X.[Nguyen Xuan], Zhou, S.[Shuo], Chan, J.[Jeffrey], Bailey, J.[James],
Can high-order dependencies improve mutual information based feature selection?,
PR(53), No. 1, 2016, pp. 46-58.
Elsevier DOI 1602
Feature selection BibRef

Zhao, J.[Ji], Wang, L.T.[Lian-Tao], Cabral, R.S., de la Torre, F.,
Feature and Region Selection for Visual Learning,
IP(25), No. 3, March 2016, pp. 1084-1094.
IEEE DOI 1602
image processing BibRef

Yuan, Y., Lin, J., Wang, Q.,
Dual-Clustering-Based Hyperspectral Band Selection by Contextual Analysis,
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IEEE DOI 1603
Context BibRef

Villela, S.M.[Saulo Moraes], de Castro Leite, S.[Saul], Neto, R.F.[Raul Fonseca],
Incremental p-margin algorithm for classification with arbitrary norm,
PR(55), No. 1, 2016, pp. 261-272.
Elsevier DOI 1604
Large margin classifiers BibRef

Zhang, X.[Xiao], Mei, C.L.[Chang-Lin], Chen, D.G.[De-Gang], Li, J.H.[Jin-Hai],
Feature selection in mixed data: A method using a novel fuzzy rough set-based information entropy,
PR(56), No. 1, 2016, pp. 1-15.
Elsevier DOI 1604
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Aksakalli, V.[Vural], Malekipirbazari, M.[Milad],
Feature selection via binary simultaneous perturbation stochastic approximation,
PRL(75), No. 1, 2016, pp. 41-47.
Elsevier DOI 1604
Classification BibRef

Armanfard, N.[Narges], Reilly, J.P.[James P.], Komeili, M.[Majid],
Local Feature Selection for Data Classification,
PAMI(38), No. 6, June 2016, pp. 1217-1227.
IEEE DOI 1605
Feature extraction. Localized method. BibRef

Wang, J., Wang, W., Wang, R., Gao, W.,
CSPS: An Adaptive Pooling Method for Image Classification,
MultMed(18), No. 6, June 2016, pp. 1000-1010.
IEEE DOI 1605
Dictionaries BibRef

Sayed, S.A.F.[Safinaz Abd_El-Fattah], Nabil, E.[Emad], Badr, A.[Amr],
A binary clonal flower pollination algorithm for feature selection,
PRL(77), No. 1, 2016, pp. 21-27.
Elsevier DOI 1606
Feature selection BibRef

Dornaika, F.[Fadi], El Traboulsi, Y.[Youssof], Assoum, A.,
Inductive and flexible feature extraction for semi-supervised pattern categorization,
PR(60), No. 1, 2016, pp. 275-285.
Elsevier DOI 1609
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FSLCV14(III: 122-137).
Springer DOI 1504
Feature extraction BibRef

Dornaika, F.[Fadi], El Traboulsi, Y.[Youssof], Cases, B., Assoum, A.,
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Li, L.[Liling], Du, L.[Lan], Zhang, W.[Wei], He, H.[Hua], Wang, P.H.[Peng-Hui],
Enhancing information discriminant analysis: Feature extraction with linear statistical model and information-theoretic criteria,
PR(60), No. 1, 2016, pp. 554-570.
Elsevier DOI 1609
Feature transformation BibRef

Ghaemi, M.[Manizheh], Feizi-Derakhshi, M.R.[Mohammad-Reza],
Feature selection using Forest Optimization Algorithm,
PR(60), No. 1, 2016, pp. 121-129.
Elsevier DOI 1609
Feature selection BibRef

Wang, Y.T.[Yin-Tong], Wang, J.D.[Jian-Dong], Liao, H.[Hao], Chen, H.Y.[Hai-Yan],
An efficient semi-supervised representatives feature selection algorithm based on information theory,
PR(61), No. 1, 2017, pp. 511-523.
Elsevier DOI 1705
Feature selection BibRef

Sheikhpour, R.[Razieh], Sarram, M.A.[Mehdi Agha], Gharaghani, S.[Sajjad], Chahooki, M.A.Z.[Mohammad Ali Zare],
A Survey on semi-supervised feature selection methods,
PR(64), No. 1, 2017, pp. 141-158.
Elsevier DOI 1701
Semi-supervised learning BibRef

Das, A.[Ayan], Das, S.[Swagatam],
Feature weighting and selection with a Pareto-optimal trade-off between relevancy and redundancy,
PRL(88), No. 1, 2017, pp. 12-19.
Elsevier DOI 1703
Feature selection BibRef

Li, F.[Feng], Miao, D.Q.[Duo-Qian], Pedrycz, W.[Witold],
Granular multi-label feature selection based on mutual information,
PR(67), No. 1, 2017, pp. 410-423.
Elsevier DOI 1704
Granular computing BibRef

Kifah, S.[Saif], Abdullah, S.[Salwani], Arajy, Y.Z.[Yahya Z.],
Solving feature selection problem using intelligent double treatment iterative composite neighbourhood structure algorithm,
IJCVR(7), No. 3, 2017, pp. 255-275.
DOI Link 1704
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Jia, Y., Ma, J., Gan, L.,
Combined Optimization of Feature Reduction and Classification for Radiometric Identification,
SPLetters(24), No. 5, May 2017, pp. 584-588.
IEEE DOI 1704
feature extraction BibRef

Liu, C.[Chuan], Wang, W.Y.[Wen-Yong], Zhao, Q.A.[Qi-Ang], Shen, X.M.[Xiao-Ming], Konan, M.[Martin],
A new feature selection method based on a validity index of feature subset,
PRL(92), No. 1, 2017, pp. 1-8.
Elsevier DOI 1705
Feature selection. BibRef

Yuan, M.S.[Ming-Shun], Yang, Z.J.[Zi-Jiang], Huang, G.Z.[Guang-Zao], Ji, G.[Guoli],
Feature selection by maximizing correlation information for integrated high-dimensional protein data,
PRL(92), No. 1, 2017, pp. 17-24.
Elsevier DOI 1705
Feature, selection BibRef

Yang, B.[Bin], Lu, Y.L.[Yu-Liang], Zhu, K.L.[Kai-Long], Yang, G.Z.[Guo-Zheng], Liu, J.W.[Jing-Wei], Yin, H.B.[Hai-Bo],
Feature Selection Based on Modified Bat Algorithm,
IEICE(E100-D), No. 8, August 2017, pp. 1860-1869.
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Xie, G.S.[Guo-Sen], Zhang, X.Y.[Xu-Yao], Yan, S.C.[Shui-Cheng], Liu, C.L.[Cheng-Lin],
SDE: A Novel Selective, Discriminative and Equalizing Feature Representation for Visual Recognition,
IJCV(124), No. 2, September 2017, pp. 145-168.
Springer DOI 1708
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Xie, G.S.[Guo-Sen], Zhang, X.Y.[Xu-Yao], Shu, X.B.[Xiang-Bo], Yan, S.C.[Shui-Cheng], Liu, C.L.[Cheng-Lin],
Task-Driven Feature Pooling for Image Classification,
ICCV15(1179-1187)
IEEE DOI 1602
Encoding BibRef

Prasad, Y.[Yamuna], Khandelwal, D.[Dinesh], Biswas, K.K.,
Max-Margin feature selection,
PRL(95), No. 1, 2017, pp. 51-57.
Elsevier DOI 1708
Feature selection BibRef

Chen, S., Yang, J., Luo, L., Wei, Y., Zhang, K., Tai, Y.,
Low-Rank Latent Pattern Approximation With Applications to Robust Image Classification,
IP(26), No. 11, November 2017, pp. 5519-5530.
IEEE DOI 1709
Feature extraction, Image reconstruction, Lighting, Measurement, Robustness, Testing, Training, ADMM, BibRef

Hafiz, F.[Faizal], Swain, A.[Akshya], Patel, N.[Nitish], Naik, C.[Chirag],
A two-dimensional (2-D) learning framework for Particle Swarm based feature selection,
PR(76), No. 1, 2018, pp. 416-433.
Elsevier DOI 1801
Classification BibRef

Liu, M., Xu, C., Luo, Y., Xu, C., Wen, Y., Tao, D.,
Cost-Sensitive Feature Selection by Optimizing F-Measures,
IP(27), No. 3, March 2018, pp. 1323-1335.
IEEE DOI 1801
Computational modeling, Cost function, Feature extraction, Prediction algorithms, imbalanced data BibRef

Huang, J., Li, G., Huang, Q., Wu, X.,
Joint Feature Selection and Classification for Multilabel Learning,
Cyber(48), No. 3, March 2018, pp. 876-889.
IEEE DOI 1802
Algorithm design and analysis, Computers, Control engineering, Correlation, Feature extraction, Prediction algorithms, Transforms, shared features BibRef

Zheng, K.F.[Kang-Feng], Wang, X.J.[Xiu-Juan],
Feature selection method with joint maximal information entropy between features and class,
PR(77), 2018, pp. 20-29.
Elsevier DOI 1802
BPSO, Entropy, Feature selection, Maximal information coefficient BibRef

Gao, W.[Wanfu], Hu, L.[Liang], Zhang, P.[Ping],
Class-specific mutual information variation for feature selection,
PR(79), 2018, pp. 328-339.
Elsevier DOI 1804
Feature selection, Information theory, Dynamic change, Classification BibRef

Gao, W.[Wanfu], Hu, L.[Liang], Zhang, P.[Ping], He, J.[Jialong],
Feature selection considering the composition of feature relevancy,
PRL(112), 2018, pp. 70-74.
Elsevier DOI 1809
Feature selection, Information theory, Classification, Composition of feature relevancy BibRef

Zhu, H.B.[Hong-Bin], Qian, H.[Hua], Luo, X.L.[Xi-Liang], Yang, Y.[Yang],
Adaptive Queuing Censoring for Big Data Processing,
SPLetters(25), No. 5, May 2018, pp. 610-614.
IEEE DOI 1805
Big Data, queueing theory, regression analysis, wireless sensor networks, adaptive queuing censoring, parameter estimation BibRef

Li, X.R.[Xiang-Rui], Zhu, D.X.[Dong-Xiao],
Robust feature selection via L2,1-norm in finite mixture of regression,
PRL(108), 2018, pp. 15-22.
Elsevier DOI 1805
Finite mixture of regression, Feature selection, Non-convex optimization BibRef

Li, Z.W.[Zhu-Wen], Cheong, L.F.[Loong-Fah], Yang, S.G.[Shuo-Guang], Toh, K.C.[Kim-Chuan],
Simultaneous Clustering and Model Selection: Algorithm, Theory and Applications,
PAMI(40), No. 8, August 2018, pp. 1964-1978.
IEEE DOI 1807
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Earlier: A1, A3, A2, A4:
Simultaneous Clustering and Model Selection for Tensor Affinities,
CVPR16(5347-5355)
IEEE DOI 1612
Analytical models, Clustering algorithms, Eigenvalues and eigenfunctions, Optimization, Robustness, segmentation BibRef

Li, Z.W.[Zhu-Wen], Cheong, L.F.[Loong-Fah], Zhou, S.Z.Y.[Steven Zhi-Ying],
SCAMS: Simultaneous Clustering and Model Selection,
CVPR14(264-271)
IEEE DOI 1409
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Yu, T., Guo, C., Wang, L., Xiang, S., Pan, C.,
Self-Paced AutoEncoder,
SPLetters(25), No. 7, July 2018, pp. 1054-1058.
IEEE DOI 1807
feature extraction, image classification, knowledge acquisition, unsupervised learning, TSPAE, video analysis BibRef

Talukdar, U.[Upasana], Hazarika, S.M.[Shyamanta M], Gan, J.Q.[John Q.],
A Kernel Partial least square based feature selection method,
PR(83), 2018, pp. 91-106.
Elsevier DOI 1808
Feature selection, Kernel partial least square, Regression coefficients, Relevance, Classification BibRef

Zhang, C., Sang, J., Zhu, G., Tian, Q.,
Bundled Local Features for Image Representation,
CirSysVideo(28), No. 8, August 2018, pp. 1719-1726.
IEEE DOI 1808
Encoding, Shape, Image representation, Feature extraction, Image reconstruction, Correlation, Bundled features, feature selection BibRef

Huang, R.[Rui], Jiang, W.D.[Wei-Dong], Sun, G.L.[Guang-Ling],
Manifold-based constraint Laplacian score for multi-label feature selection,
PRL(112), 2018, pp. 346-352.
Elsevier DOI 1809
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Huang, R.[Rui], Wu, Z.J.[Zhe-Jun],
Multi-label feature selection via manifold regularization and dependence maximization,
PR(120), 2021, pp. 108149.
Elsevier DOI 2109
Multi-label learning, Feature selection, Sparse regression, Manifold regularization, Dependence maximization BibRef

Sharmin, S.[Sadia], Shoyaib, M.[Mohammad], Ali, A.A.[Amin Ahsan], Khan, M.A.H.[Muhammad Asif Hossain], Chae, O.[Oksam],
Simultaneous feature selection and discretization based on mutual information,
PR(91), 2019, pp. 162-174.
Elsevier DOI 1904
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Earlier: A1, A3, A4, A2, Only:
Feature Selection and Discretization based on Mutual Information,
IVPR17(1-6)
IEEE DOI 1704
Feature selection, Mutual information, Bias, Dynamic discretization. Classification algorithms BibRef

Yu, E., Sun, J., Li, J., Chang, X., Han, X., Hauptmann, A.G.,
Adaptive Semi-Supervised Feature Selection for Cross-Modal Retrieval,
MultMed(21), No. 5, May 2019, pp. 1276-1288.
IEEE DOI 1905
data handling, feature selection, graph theory, learning (artificial intelligence), matrix algebra, optimisation, feature selection BibRef

Wang, S., Ding, Z., Fu, Y.,
Discerning Feature Supported Encoder for Image Representation,
IP(28), No. 8, August 2019, pp. 3728-3738.
IEEE DOI 1907
feature selection, image classification, image coding, image representation, learning (artificial intelligence), learning systems BibRef

Thuy, N.N.[Nguyen Ngoc], Wongthanavasu, S.[Sartra],
A new approach for reduction of attributes based on stripped quotient sets,
PR(97), 2020, pp. 106999.
Elsevier DOI 1910
Rough set, Attribute reduction, Core attributes, Stripped quotient set, Attribute significance measure BibRef

Georges, N.[Nicolas], Mhiri, I.[Islem], Rekik, I.[Islem],
Identifying the best data-driven feature selection method for boosting reproducibility in classification tasks,
PR(101), 2020, pp. 107183.
Elsevier DOI 2003
Feature selection methods, Multi-graph topological analysis, Feature reproducibility, Biomarker discovery, Cross-validation BibRef

Maleki, M.H.[Mohammad Hassan], Hodtani, G.A.[Ghosheh Abed], Hashemi, S.H.O.[Seyed Hesam Odin],
KSR-BOF: a new and exemplified method (as KSRs method) for image classification,
IET-IPR(14), No. 5, 17 April 2020, pp. 853-861.
DOI Link 2004
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Rivera-López, R.[Rafael], Mezura-Montes, E.[Efrén], Canul-Reich, J.[Juana], Cruz-Chávez, M.A.[Marco Antonio],
A permutational-based Differential Evolution algorithm for feature subset selection,
PRL(133), 2020, pp. 86-93.
Elsevier DOI 2005
Machine learning, Evolutionary algorithms, Wrapper scheme BibRef

Zhou, P.[Peng], Du, L.[Liang], Li, X.J.[Xue-Jun], Shen, Y.D.[Yi-Dong], Qian, Y.H.[Yu-Hua],
Unsupervised feature selection with adaptive multiple graph learning,
PR(105), 2020, pp. 107375.
Elsevier DOI 2006
Feature selection, Multiple graph learning, Consensus learning BibRef

Zhao, J.[Jie], Liang, J.M.[Jia-Ming], Dong, Z.N.[Zhen-Ning], Tang, D.Y.[De-Yu], Liu, Z.[Zhen],
Accelerating information entropy-based feature selection using rough set theory with classified nested equivalence classes,
PR(107), 2020, pp. 107517.
Elsevier DOI 2008
Feature selection, Rough set theory, Attribute reduction, Information entropy BibRef

Zhao, J.[Jie], Wu, D.Y.[Dai-Yang], Wu, J.X.[Jia-Xin], Ye, W.H.[Wen-Hao], Huang, F.L.[Fa-Liang], Wang, J.H.[Jia-Hai], See-To, E.W.K.[Eric W.K.],
Consistency approximation: Incremental feature selection based on fuzzy rough set theory,
PR(155), 2024, pp. 110652.
Elsevier DOI 2408
Fuzzy rough set, Incremental feature selection, Consistency approximation BibRef

Thom de Souza, R.C.[Rodrigo Clemente], de Macedo, C.A.[Camila Andrade], dos Santos Coelho, L.[Leandro], Pierezan, J.[Juliano], Mariani, V.C.[Viviana Cocco],
Binary coyote optimization algorithm for feature selection,
PR(107), 2020, pp. 107470.
Elsevier DOI 2008
Wrapper feature selection, Classification, Coyote optimization algorithm (COA), Bio-inspired optimization, Binary COA BibRef

Aziz, F.[Furqan], Ullah, A.[Afan], Shah, F.[Faiza],
Feature selection and learning for graphlet kernel,
PRL(136), 2020, pp. 63-70.
Elsevier DOI 2008
Structural characterization of families of graphs, Small world graphs, complex networks, Graph algorithms BibRef

Ircio, J.[Josu], Lojo, A.[Aizea], Mori, U.[Usue], Lozano, J.A.[Jose A.],
Mutual information based feature subset selection in multivariate time series classification,
PR(108), 2020, pp. 107525.
Elsevier DOI 2008
Multivariate time series, Supervised classification, Feature susbset selection, Mutual information BibRef

Solorio-Fernández, S.[Saúl], Martínez-Trinidad, J.F.[José Fco.], Carrasco-Ochoa, J.A.[J. Ariel],
A Supervised Filter Feature Selection method for mixed data based on Spectral Feature Selection and Information-theory redundancy analysis,
PRL(138), 2020, pp. 321-328.
Elsevier DOI 2010
Supervised feature selection, Mixed data, Filter feature subset selection, Redundancy analysis BibRef

Ben Said, F.[Fatma], Alimi, A.M.[Adel M.],
Online feature selection system for big data classification based on multi-objective automated negotiation,
PR(110), 2021, pp. 107629.
Elsevier DOI 2011
Feature selection, Online learning, Multi-objective automated negotiation, Trust, Classification, Big data BibRef

García-Pedrajas, N.[Nicolás], del Castillo, J.A.R.[Juan A. Romero], Cerruela-García, G.[Gonzalo],
SI(FS)2: Fast simultaneous instance and feature selection for datasets with many features,
PR(111), 2021, pp. 107723.
Elsevier DOI 2012
Instance selection, Feature selection, Evolutionary algorithms, nearest neighbor rule BibRef

Lall, S.[Snehalika], Sinha, D.[Debajyoti], Ghosh, A.[Abhik], Sengupta, D.[Debarka], Bandyopadhyay, S.[Sanghamitra],
Stable feature selection using copula based mutual information,
PR(112), 2021, pp. 107697.
Elsevier DOI 2102
Copula, Feature selection, Mutual information, Stability, Classification accuracy BibRef

Zhang, X.Y.[Xian-Yong], Fan, Y.R.[Yun-Rui], Yang, J.L.[Ji-Lin],
Feature selection based on fuzzy-neighborhood relative decision entropy,
PRL(146), 2021, pp. 100-107.
Elsevier DOI 2105
Rough set, Fuzzy neighborhood rough set, Feature selection, Relative decision entropy, Uncertainty measurement, Granulation monotonicity BibRef

Viharos, Z.J.[Zsolt János], Kis, K.B.[Krisztián Balázs], Fodor, Á.[Ádám], Büki, M.I.[Máté István],
Adaptive, Hybrid Feature Selection (AHFS),
PR(116), 2021, pp. 107932.
Elsevier DOI 2106
Adaptive, Hybrid Feature Selection (AHFS), Combination of methods, Statistics, Information theory, Exhausting evaluation BibRef

Shpakova, T.[Tatiana], Sokolovska, N.[Nataliya],
Probabilistic personalised cascade with abstention,
PRL(147), 2021, pp. 8-15.
Elsevier DOI 2106
Graphical models, Learning under budget, Feature selection, Cascade classifier BibRef

Zhong, W.C.[Wei-Chan], Chen, X.J.[Xiao-Jun], Wu, Q.Y.[Qing-Yao], Yang, M.[Min], Huang, J.Z.[Joshua Zhexue],
Selection of diverse features with a diverse regularization,
PR(120), 2021, pp. 108154.
Elsevier DOI 2109
Feature selection, Supervised feature selection, Diverse feature, Regularization BibRef

Komeili, M.[Majid], Armanfard, N.[Narges], Hatzinakos, D.[Dimitrios],
Multiview Feature Selection for Single-View Classification,
PAMI(43), No. 10, October 2021, pp. 3573-3586.
IEEE DOI 2109
Feature extraction, Training, Dimensionality reduction, Correlation, Error analysis, Biomedical imaging, Feature selection, classification BibRef

Zhang, S.[Sikai], Lang, Z.Q.[Zi-Qiang],
Orthogonal least squares based fast feature selection for linear classification,
PR(123), 2022, pp. 108419.
Elsevier DOI 2112
Feature selection, Orthogonal least squares, Canonical correlation analysis, Linear discriminant analysis, Feature interaction BibRef

Dai, J.D.[Jin-Dou], Wu, Y.W.[Yu-Wei], Gao, Z.[Zhi], Jia, Y.D.[Yun-De],
Infinite-dimensional feature aggregation via a factorized bilinear model,
PR(124), 2022, pp. 108397.
Elsevier DOI 2203
Feature aggregation, Infinite-dimensional features, Non-approximate method, Second-order statistics BibRef

Wan, J.H.[Ji-Hong], Chen, H.M.[Hong-Mei], Li, T.R.[Tian-Rui], Huang, W.[Wei], Li, M.[Min], Luo, C.[Chuan],
R2CI: Information theoretic-guided feature selection with multiple correlations,
PR(127), 2022, pp. 108603.
Elsevier DOI 2205
Feature selection, Information theory, Relevance, Redundancy, Complementarity, Interaction BibRef

Barisin, T.[Tin], Jung, C.[Christian], Müsebeck, F.[Franziska], Redenbach, C.[Claudia], Cao, W.M.[Wen-Ming], Zhang, Z.F.[Zhong-Fan], Liu, C.[Cheng], Li, R.[Rui], Jiao, Q.F.[Qian-Fen], Yu, Z.W.[Zhi-Wen], Wong, H.S.[Hau-San],
Unsupervised discriminative feature learning via finding a clustering-friendly embedding space,
PR(129), 2022, pp. 108768.
Elsevier DOI 2206
Deep clustering, Unsupervised learning, Generative adversarial networks, Siamese network BibRef

Souza, F.[Francisco], Premebida, C.[Cristiano], Araújo, R.[Rui],
High-order conditional mutual information maximization for dealing with high-order dependencies in feature selection,
PR(131), 2022, pp. 108895.
Elsevier DOI 2208
Feature selection, Mutual information, Information theory BibRef

Liu, Y.[Yi], Qin, W.[Wei], Zheng, Q.B.[Qi-Bin], Li, G.S.[Gen-Song], Li, M.M.[Meng-Meng],
An Interpretable Feature Selection Based on Particle Swarm Optimization,
IEICE(E105-D), No. 8, August 2022, pp. 1495-1500.
WWW Link. 2207
BibRef

Eskandari, S., Seifaddini, M.,
Online and offline streaming feature selection methods with bat algorithm for redundancy analysis,
PR(133), 2023, pp. 109007.
Elsevier DOI 2210
Feature selection, Online feature selection, Streamwise feature selection, Dimension reduction, Bat algorithm BibRef

Liu, P.F.[Peng-Fei], Guo, Y.H.[Yu-Han], Tan, J.[Jiubin], Wang, W.[Weibo],
Loss reweight in scale dimension: A simple while effective feature selection strategy for anchor-free detectors,
IVC(128), 2022, pp. 104593.
Elsevier DOI 2212
Object detection, Feature selection, Deep learning BibRef

Deng, Y.X.[Yu-Xin], Ma, J.Y.[Jia-Yi],
ReDFeat: Recoupling Detection and Description for Multimodal Feature Learning,
IP(32), 2023, pp. 591-602.
IEEE DOI 2301
Feature extraction, Detectors, Training, Reliability, Representation learning, Optimization, Benchmark testing, image matching BibRef

Wang, Y.[Yadi], Wang, J.[Jun], Tao, D.C.[Da-Cheng],
Neurodynamics-driven supervised feature selection,
PR(136), 2023, pp. 109254.
Elsevier DOI 2301
Feature selection, Biconvex Optimization, Information-theoretic measures, Neurodynamic optimization BibRef

Wu, T.[Ting], Hao, Y.H.[Yi-Hang], Yang, B.[Bo], Peng, L.Z.[Li-Zhi],
ECM-EFS: An ensemble feature selection based on enhanced co-association matrix,
PR(139), 2023, pp. 109449.
Elsevier DOI 2304
Ensemble feature selection, Machine learning, Feature kernel, Relative-co-association matrix (RCM) BibRef

Liu, Z.G.[Zhao-Geng], Yang, J.L.[Jie-Long], Wang, L.[Li], Chang, Y.[Yi],
A novel relation aware wrapper method for feature selection,
PR(140), 2023, pp. 109566.
Elsevier DOI 2305
Feature selection, Sample relation, Feature relation, Classification BibRef

Wang, P.[Peng], Xue, B.[Bing], Liang, J.[Jing], Zhang, M.J.[Meng-Jie],
Feature clustering-Assisted feature selection with differential evolution,
PR(140), 2023, pp. 109523.
Elsevier DOI 2305
Differential evolution, Feature selection, Multiple optimal feature subsets, Classification BibRef

Lahmar, I.[Ines], Zaier, A.[Aida], Yahia, M.[Mohamed], Boaullegue, R.[Ridha],
A Novel Improved Binary Harris Hawks Optimization For High dimensionality Feature Selection,
PRL(171), 2023, pp. 170-176.
Elsevier DOI 2306
Harris hawk optimizer, Simulated annealing, Chaotic opposition-Based initialization, Feature selection, Classification BibRef

Hou, C.P.[Chen-Ping], Fan, R.D.[Rui-Dong], Zeng, L.L.[Ling-Li], Hu, D.[Dewen],
Adaptive Feature Selection With Augmented Attributes,
PAMI(45), No. 8, August 2023, pp. 9306-9324.
IEEE DOI 2307
Feature extraction, Optimization, Principal component analysis, Data models, Covariance matrices, Convergence, reusability BibRef

Chen, Y.L.[Yi-Lin], Gao, B.[Bo], Lu, T.[Tao], Li, H.[Hui], Wu, Y.Q.[Yi-Qi], Zhang, D.J.[De-Jun], Liao, X.Y.[Xiang-Yun],
A Hybrid Binary Dragonfly Algorithm with an Adaptive Directed Differential Operator for Feature Selection,
RS(15), No. 16, 2023, pp. 3980.
DOI Link 2309
BibRef

Takhanov, R.[Rustem], Abylkairov, Y.S.[Y. Sultan], Wan, J.H.[Ji-Hong], Chen, H.M.[Hong-Mei], Li, T.R.[Tian-Rui], Li, M.[Min], Yang, X.L.[Xiao-Ling],
High-order interaction feature selection for classification learning: A robust knowledge metric perspective,
PR(143), 2023, pp. 109733.
Elsevier DOI 2310
Feature selection, Fuzzy rough set, High-order interaction, Robust knowledge metric, Uncertainty measures, Classification BibRef

Yuan, L.X.[Li-Xin], Mei, C.[Cheng], Wang, W.H.[Wen-Hai], Lu, T.[Tong],
Feature Selection Based on Intrusive Outliers Rather Than All Instances,
IP(33), 2024, pp. 809-824.
IEEE DOI 2402
Feature extraction, Training, Task analysis, Solid modeling, Mutual information, Measurement, Face recognition, classification BibRef

Zhong, J.Y.[Jing-Yu], Shang, R.H.[Rong-Hua], Xu, S.H.[Song-Hua], Li, Y.Y.[Yang-Yang],
Graph embedding orthogonal decomposition: A synchronous feature selection technique based on collaborative particle swarm optimization,
PR(152), 2024, pp. 110453.
Elsevier DOI 2405
Clustering label orthogonal decomposition, Graph-embedded, Local structure preserving, Particle swarm optimization BibRef

Li, Q.[Qing], Zhao, S.[Shuai], He, T.J.[Teng-Jiao], Wen, J.M.[Jin-Ming],
A simple and efficient filter feature selection method via document-term matrix unitization,
PRL(181), 2024, pp. 23-29.
Elsevier DOI 2405
Filter feature selection, Text processing, Document-term matrix unitization BibRef

Li, C.N.[Chun-Na], Huang, L.W.[Ling-Wei], Shao, Y.H.[Yuan-Hai], Guo, T.T.[Ting-Ting], Mao, Y.[Yu],
Feature selection by Universum embedding,
PR(153), 2024, pp. 110514.
Elsevier DOI 2405
Feature selection, Universum, Support vector machine, Universum support vector machine, Embedded support vector machine BibRef

Blal, M.A.[Mohamed Ait], Yahyaouy, A.[Ali], Abdelouahed, S.M.[Sabri My], Verde, R.[Rosanna],
Robust Multi-view Multi- Modal Ensemble for Improved Image Classification with Feature Selection,
ISCV24(1-7)
IEEE DOI 2408
Training, Learning systems, Noise, Training data, Feature extraction, Noise measurement, ensemble learning BibRef

Yuan, A.[Aihong], Huang, J.H.[Jia-Hao], Wei, C.[Chen], Zhang, W.J.[Wen-Jie], Zhang, N.[Naidan], You, M.B.[Meng-Bo],
Unsupervised Feature Selection via Feature-Grouping and Orthogonal Constraint,
ICPR22(720-726)
IEEE DOI 2212
Correlation, Source coding, Machine learning, Benchmark testing, Feature extraction, Data structures, Data models BibRef

Biaggi, L.[Lucas], Papa, J.P.[João P.], Costa, K.A.P.[Kelton A. P], Pereira, D.R.[Danillo R.], Passos, L.A.[Leandro A.],
FEMa-FS: Finite Element Machines for Feature Selection,
ICPR22(1784-1791)
IEEE DOI 2212
Learning systems, Feature extraction, Computer networks, Finite element analysis, Security, Finite Element Method BibRef

Jaiswal, S.[Shantanu], Fernando, B.[Basura], Tan, C.[Cheston],
TDAM: Top-Down Attention Module for Contextually Guided Feature Selection in CNNs,
ECCV22(XXV:259-276).
Springer DOI 2211
BibRef

Panda, P.[Pranoy], Kancheti, S.S.[Sai Srinivas], Balasubramanian, V.N.[Vineeth N.],
Instance-wise Causal Feature Selection for Model Interpretation,
CiV21(1756-1759)
IEEE DOI 2109
Training, Measurement, Visualization, Feature extraction, Linear programming, Entropy BibRef

Kawamura, N.[Naoki], Kubota, S.[Susumu],
Sample-Dependent Distance for 1: N Identification via Discriminative Feature Selection,
ICPR21(3365-3371)
IEEE DOI 2105
Training, Feature extraction, Extraterrestrial measurements, Task analysis BibRef

König, G.[Gunnar], Molnar, C.[Christoph], Bischl, B.[Bernd], Grosse-Wentrup, M.[Moritz],
Relative Feature Importance,
ICPR21(9318-9325)
IEEE DOI 2105
Training, Perturbation methods, Machine learning, feature importance, causality BibRef

Xie, X.[Xiang], Stork, W.[Wilhelm],
Watermelon: a Novel Feature Selection Method Based on Bayes Error Rate Estimation and a New Interpretation of Feature Relevance and Redundancy,
ICPR21(1360-1367)
IEEE DOI 2105
Correlation coefficient, Correlation, Error analysis, Redundancy, Estimation, Feature extraction BibRef

Shen, W., Li, F., Liu, R.,
Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks,
SDL-CV19(733-737)
IEEE DOI 2004
convolutional neural nets, feature extraction, image classification, learning (artificial intelligence), Information flow BibRef

Mas, I., Morros, R., Vilaplana, V.,
Picking Groups Instead of Samples: A Close Look at Static Pool-Based Meta-Active Learning,
MDALC19(1354-1362)
IEEE DOI 2004
feature selection, pattern classification, supervised learning, unsupervised learning, static pool-based meta-active learning, Learning under constraints BibRef

Zaeemzadeh, A.[Alireza], Joneidi, M.[Mohsen], Rahnavard, N.[Nazanin], Shah, M.[Mubarak],
Iterative Projection and Matching: Finding Structure-Preserving Representatives and Its Application to Computer Vision,
CVPR19(5409-5418).
IEEE DOI 2002
BibRef

Rodrigues dos Santos, F.C.[Fábio Cosme], Henriques Librantz, A.F.[André Felipe], Sassi, R.J.[Renato José],
An Approach to Clustering Using the Expectation-Maximization and Selection of Attributes ReliefF Applied to Water Treatment Plants process,
CIARP17(558-565).
Springer DOI 1802
ReliefF algorithm. BibRef

Ghosh, S.[Soumen], Sai Prasad, P.S.V.S., Rao, C.R.[C. Raghavendra],
Third Order Backward Elimination Approach for Fuzzy-Rough Set Based Feature Selection,
PReMI17(254-262).
Springer DOI 1711
BibRef

Chowdhury, H.A.[Hussain A.], Bhattacharyya, D.K.[Dhruba K.],
mRMR+: An Effective Feature Selection Algorithm for Classification,
PReMI17(424-430).
Springer DOI 1711
BibRef

Mi, J.X.[Jian-Xun], Fu, Q.K.[Qian-Kun], Li, W.S.[Wei-Sheng],
Adaptive Class Preserving Representation for Image Classification,
CVPR17(2624-2632)
IEEE DOI 1711
Adaptation models, Correlation, Image classification, Optimization, Telecommunications, Training BibRef

Yassine, A., Mohamed, C., Zinedine, A.,
Feature selection based on pairwise evalution,
ISCV17(1-6)
IEEE DOI 1710
decision trees, feature selection, AUC, area under the ROC curve, decision tree algorithm, hybrid filter-wrapper algorithm, pairwise evaluation, pairwise feature selection, BibRef