A survey on feature selection methods

Guyon, 2003, An introduction to variable and feature selection, J Mach Learn Res, 3, 1157

Guyon, 2002, Gene selection for cancer classification using support vector machines, Mach Learn, 46, 389, 10.1023/A:1012487302797

Ding, 2005, Minimum redundancy feature selection from microarray gene expression data, J Bioinform Comput Biol, 3, 185, 10.1142/S0219720005001004

Chuang, 2008, Improved binary PSO for feature selection using gene expression data, Comput Biol Chem, 32, 29, 10.1016/j.compbiolchem.2007.09.005

Lazar, 2012, A survey on filter techniques for feature selection in gene expression microarray analysis, IEEE/ACM Trans Comput Biol Bioinform, 9, 10.1109/TCBB.2012.33

Alpaydin, 2004

Law, 2004, Simultaneous feature selection and clustering using mixture models, IEEE Trans Pattern Anal Mach Intell, 26, 10.1109/TPAMI.2004.71

Kohavi, 1997, Wrappers for feature subset selection, Artif Intell, 97, 273, 10.1016/S0004-3702(97)00043-X

Langley P. Selection of relevant features in machine learning. In: AAAI fall symp relevance; 1994.

Blum, 1997, Selection of relevant features and examples in machine learning, Artif Intell, 97, 245, 10.1016/S0004-3702(97)00063-5

John GH, Kohavi R, Pfleger K. Irrelevant features and the subset selection problem. In: Proc 11th int conf mach learn; 1994. p. 121–9.

Battiti, 1994, Using mutual information for selecting features in supervised neural net learning, IEEE Trans Neural Networks, 5, 10.1109/72.298224

Forman, 2003, An extensive empirical study of feature selection metrics for text classification, J Mach Learn Res, 3, 1289

Kwak, 2002, Input feature selection for classification problems, IEEE Trans Neural Networks, 13, 143, 10.1109/72.977291

Comon, 1994, Independent component analysis a new concept?, Signal Process, 36, 287, 10.1016/0165-1684(94)90029-9

Torkkola, 2003, On feature extraction by non-parametric mutual information maximization, J Mach Learn Res, 3, 1415

Fleuret, 2004, Fast binary feature selection with conditional mutual information, Mach Learn Res, 5, 1531

Bekkerman, 2003, Distributional word clusters vs. words for text categorization, J Mach Learn Res, 3, 1245

Caruana, 2003, Benefitting from the variables that variable selection discards, J Mach Learn Res, 3, 1245

Koller D, Sahami M. Towards optimal feature selection. In: ICML, vol. 96; 1996. p. 284–92.

Davidson JL, Jalan J. Feature selection for steganalysis using the mahalonobis distance. In: Proc SPIE 7541, Media Forensics and Security II 7541; 2010.

Yang Y, Perdersen JO. A comparative study on feature selection in text categorization. International conference on machine learning; 1997.

Javed, 2010, Feature selection based on class-dependent densities for high-dimensional binary data, IEEE Trans Knowl Data Eng, 24

Peng, 2005, Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy, IEEE Trans Pattern Anal Mach Intell, 27

Kira K, Rendell LA. The feature selection problem: traditional methods and a new algorithm. In: Proceedings of tenth national conference on artificial intelligence; 1992. p. 129–34.

Acuna, 2003, A comparison of feature selection procedures for classifier based on kernel density estimation, Proc Comput Commun Control Technol, 1, 468

Stoppiglia, 2003, Ranking a random feature for variable and feature selection, J Mach Res, 3, 1399

Liu H, Setiono R. A probabilistic approach to feature selection a filter solution. In: International conference on machine learning - ICML; 1996. p. 319–27.

Xu, 2010, Discriminative semi-supervised feature selection via manifold regularization, IEEE Trans Neural Networks, 21

Narendra, 1977, A branch and bound algorithm for feature subset selection, IEEE Trans Comput, 6, 917, 10.1109/TC.1977.1674939

Goldberg, 1989

Kennedy J, Eberhart RC. Particle swarm optimization. In: Proc IEEE int’l conf on neural networks, IV; 1995. p. 1942–1948.

Pudil, 1994, Floating search methods in feature selection, Pattern Recog Lett, 15, 1119, 10.1016/0167-8655(94)90127-9

Reunanen, 2003, Overfitting in making comparisons between variable selection methods, J Mach Learn Res, 3, 1371

Pudil, 1999, Adaptive floating search methods in feature selection, Pattern Recog Lett, 20, 1157, 10.1016/S0167-8655(99)00083-5

Sun Y, Babbs C, Delp E. A comparison of feature selection methods for the detection of breast cancers in mammograms: adaptive sequential floating search vs. genetic algorithm. Conf proc IEEE eng med biol soc, vol. 6.

Nakariyakul, 2009, An improvement on floating search algorithms for feature subset selection, Pattern Recog, 42, 1932, 10.1016/j.patcog.2008.11.018

Stearns S. On selecting features for pattern classifiers. In: Proceedings of the 3rd international conference on pattern recognition; 1976. p. 71–5.

Alexandridis, 2005, A two-stage evolutionary algorithm for variable selection in the development of rbf neural network models, Chemomet Intell Lab Syst, 75, 149, 10.1016/j.chemolab.2004.06.004

Jouan-Rimbaud D, Massart DL, Leardi R, Noord OED. Genetic algorithms as a tool for wavenumber selection in multivariate calibration. Anal Chem 67.

Yang, 1998, Feature subset selection using a genetic algorithm, IEEE Intell Syst Appl, 13, 44, 10.1109/5254.671091

Puch W, Goodman E, Pei M, Chia-Shun L, Hovland P, Enbody R. Further research on feature selection and classification using genetic algorithm. In International conference on genetic algorithm; 1993. p. 557–64.

Eshelman, 1991, The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination, 10.1016/B978-0-08-050684-5.50020-3

Cordon, 2006, Feature-based image registration by means of the chc evolutionary algorithm, Image Vis Comput, 24, 525, 10.1016/j.imavis.2006.02.002

Oliveira, 2003, A methodology for feature selection using multiobjective genetic algorithms for handwritten digit sting recognition, Int J Pattern Recog Artif Intell, 17, 903, 10.1142/S021800140300271X

Ferri, 1994, Comparative study of techniques for large-scale feature selection, Pattern Recog Pract, 403

Kudo, 2000, Comparison of algorithms that select features for pattern classifiers, Pattern Recog, 33, 327, 10.1016/S0031-3203(99)00041-2

Tu, 2006, Feature selection using pso-svm, Int J Comput Sci, 33

Alba, 2007, Gene selection in cancer classification using pso/svm and ga/svm hybrid algorithms, Evol Comput, 284

Mundra, 2010, Svm-rfe with mrmr filter for gene selection, IEEE Trans Nanobiosci, 9, 10.1109/TNB.2009.2035284

Boser B, Guyon I, Vapnik V. A training algorithm for optimal margin classifiers. In: In fifth annual workshop on computational learning theory; 1992. p. 144–52.

Chapelle O, Keerthi SS. Multi-class feature selection with support vector machines; 2008.

Archibald, 2007, Feature selection and classification of hyperspectral images with support vector machines, IEEE Geosci Remote Sens Lett, 4, 10.1109/LGRS.2007.905116

Neumann, 2005, Combined svm-based feature selection and classification, Mach Learn, 61, 129, 10.1007/s10994-005-1505-9

Setiono, 1997, Neural-network feature selector, IEEE Trans Neural Networks, 8, 654, 10.1109/72.572104

Romero, 2008, Performing feature selection with multilayer perceptrons, IEEE Trans Neural Networks, 19, 10.1109/TNN.2007.909535

Stracuzzi, 2004, Randomized variable elimination, J Mach Learn, 5, 1331

Wu, 2009, Uninformation variable elimination and successive projections algorithm in mid-infrared spectra wavenumber selection, Image Signal Process

Centner, 1996, Elimination of uninformative variables for multivariate calibration, Anal Chem, 68, 3851, 10.1021/ac960321m

Alsberg, 1998, Variable selection in wavelet regression models, Anal Chim Acta, 368, 29, 10.1016/S0003-2670(98)00194-9

Peng, 2009, Lazy learner text categorization algorithm based on embedded feature selection, J Syst Eng Electron, 20, 651

Xing E, Karp R. Cliff: clustering of high-dimensional microarray data via iterative feature filtering using normalized cuts. In: 9th International conference on intelligence systems for molecular biology; 2001.

Pudil, 1995, Feature selection based on the approximation of class densities by finite mixtures of the special type, Pattern Recog, 28, 1389, 10.1016/0031-3203(94)00009-B

Mitra, 2002, Unsupervised feature selection using feature similarity, IEEE Trans Pattern Anal Mach Intell, 24, 10.1109/34.990133

Pal, 2000, Unsupervised feature evaluation: a neuro-fuzzy approach, IEEE Trans Neural Networks, 11, 10.1109/72.839007

Zhu, 2005

Zhao Z, Liu H. Semi-supervised feature selection via spectral analysis. In: Proc 7th SIAM data mining conf (SDM); 2007. p. 641–6.

Haury, 2011, The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures, PLoS ONE, 6, e28210, 10.1371/journal.pone.0028210

T, 2010, Robust biomarker identification for cancer diagnosis with ensemble feature selection methods, Bioinformatics, 26, 392, 10.1093/bioinformatics/btp630

Dunne K, Cunningham P, Azuaje F. Solutions to instability problems with sequential wrapper-based approaches to feature selection. Tech rep, Trinity College; 2002.

Kalousis, 2007, Stability of feature selection algorithms: a study on high dimensional spaces, Knowl Inform Syst, 2, 95, 10.1007/s10115-006-0040-8

Somol, 2010, Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality, IEEE Trans Pattern Anal Mach Intell, 32, 1921, 10.1109/TPAMI.2010.34

Yang, 2011, Robust feature selection for microarray data based on multicriterion fusion, IEEE/ACM Trans Comput Biol Bioinform, 8

Dietterich, 1997, Machine learning research: four current directions, Artif Intell Mag, 18, 97

Chang, 2011, Libsvm: a library for support vector machines, ACM Trans Intell Syst Technol, 2, 1, 10.1145/1961189.1961199

Rifkin, 2004, In defense of one-vs-all classification, J Mach Learn Res, 5, 101

Haykin, 2008

Cinar E, Sahin F. A study of recent classification algorithms and a novel approach for EEG data classification. In: IEEE 2010 international conference on systems, man and cybernetics; 2010.

Loong, 2008, Criterion in selecting the clustering algorithm in radial basis functional link nets, WSEAS Trans Syst, 7, 1290

Marcos, 2008, Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry, Med Biol Eng Comput, 46, 323, 10.1007/s11517-007-0280-0

Hongyang L, He J. The application of dynamic k-means clustering algorithm in the center selection of rbf neural networks. In: Proc 3rd international conference on genetic and evolutionary computing, vol. 177; 2009. p. 488–91.

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Chandrashekar G, Sahin F. In-vivo fault prediction for rf generators using variable elimination and state-of-theart classifiers. 2012 IEEE international conference on systems, man, and cybernetics October 14–17, COEX, Seoul, Korea; 2012.