메뉴 건너뛰기




Volumn 40, Issue 1, 2014, Pages 16-28

A survey on feature selection methods

Author keywords

[No Author keywords available]

Indexed keywords


EID: 84894903349     PISSN: 00457906     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compeleceng.2013.11.024     Document Type: Article
Times cited : (3991)

References (83)
  • 1
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • I. Guyon, and A. Elisseeff An introduction to variable and feature selection J Mach Learn Res 3 2003 1157 1182
    • (2003) J Mach Learn Res , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 2
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barhill, and V. Vapnik Gene selection for cancer classification using support vector machines Mach Learn 46 2002 389 422
    • (2002) Mach Learn , vol.46 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barhill, S.3    Vapnik, V.4
  • 3
    • 17644384367 scopus 로고    scopus 로고
    • Minimum redundancy feature selection from microarray gene expression data
    • C. Ding, and H. Peng Minimum redundancy feature selection from microarray gene expression data J Bioinform Comput Biol 3 2005 185 205
    • (2005) J Bioinform Comput Biol , vol.3 , pp. 185-205
    • Ding, C.1    Peng, H.2
  • 4
    • 37549011765 scopus 로고    scopus 로고
    • Improved binary PSO for feature selection using gene expression data
    • L.-Y. Chuang, H.-W. Chang, C.-J. Tu, and C.-H. Yang Improved binary PSO for feature selection using gene expression data Comput Biol Chem 32 2008 29 38
    • (2008) Comput Biol Chem , vol.32 , pp. 29-38
    • Chuang, L.-Y.1    Chang, H.-W.2    Tu, C.-J.3    Yang, C.-H.4
  • 8
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi, and G.H. John Wrappers for feature subset selection Artif Intell 97 1997 273 324
    • (1997) Artif Intell , vol.97 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 9
    • 85061066913 scopus 로고
    • Selection of relevant features in machine learning
    • Langley P. Selection of relevant features in machine learning. In: AAAI fall symp relevance; 1994.
    • (1994) AAAI Fall Symp Relevance
    • Langley, P.1
  • 10
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • A.L. Blum, and P. Langley Selection of relevant features and examples in machine learning Artif Intell 97 1997 245 270
    • (1997) Artif Intell , vol.97 , pp. 245-270
    • Blum, A.L.1    Langley, P.2
  • 12
    • 0028468293 scopus 로고
    • Using mutual information for selecting features in supervised neural net learning
    • R. Battiti Using mutual information for selecting features in supervised neural net learning IEEE Trans Neural Networks 5 1994
    • (1994) IEEE Trans Neural Networks , vol.5
    • Battiti, R.1
  • 13
    • 2942731012 scopus 로고    scopus 로고
    • An extensive empirical study of feature selection metrics for text classification
    • G. Forman An extensive empirical study of feature selection metrics for text classification J Mach Learn Res 3 2003 1289 1306
    • (2003) J Mach Learn Res , vol.3 , pp. 1289-1306
    • Forman, G.1
  • 14
    • 0036127473 scopus 로고    scopus 로고
    • Input feature selection for classification problems
    • N. Kwak, and C.-H. Choi Input feature selection for classification problems IEEE Trans Neural Networks 13 2002 143 159
    • (2002) IEEE Trans Neural Networks , vol.13 , pp. 143-159
    • Kwak, N.1    Choi, C.-H.2
  • 15
    • 0028416938 scopus 로고
    • Independent component analysis a new concept?
    • P. Comon Independent component analysis a new concept? Signal Process 36 1994 287 314
    • (1994) Signal Process , vol.36 , pp. 287-314
    • Comon, P.1
  • 16
    • 1942450610 scopus 로고    scopus 로고
    • On feature extraction by non-parametric mutual information maximization
    • K. Torkkola On feature extraction by non-parametric mutual information maximization J Mach Learn Res 3 2003 1415 1438
    • (2003) J Mach Learn Res , vol.3 , pp. 1415-1438
    • Torkkola, K.1
  • 17
    • 33645690579 scopus 로고    scopus 로고
    • Fast binary feature selection with conditional mutual information
    • F. Fleuret Fast binary feature selection with conditional mutual information Mach Learn Res 5 2004 1531 1555
    • (2004) Mach Learn Res , vol.5 , pp. 1531-1555
    • Fleuret, F.1
  • 18
    • 77951430107 scopus 로고    scopus 로고
    • Distributional word clusters vs words for text categorization
    • R. Bekkerman, R. El-Yaniv, N. Tishby, and Y. Winter Distributional word clusters vs. words for text categorization J Mach Learn Res 3 2003 1245 1264
    • (2003) J Mach Learn Res , vol.3 , pp. 1245-1264
    • Bekkerman, R.1    El-Yaniv, R.2    Tishby, N.3    Winter, Y.4
  • 19
    • 2942734703 scopus 로고    scopus 로고
    • Benefitting from the variables that variable selection discards
    • R. Caruana, and V. de S Benefitting from the variables that variable selection discards J Mach Learn Res 3 2003 1245 1264
    • (2003) J Mach Learn Res , vol.3 , pp. 1245-1264
    • Caruana, R.1    De S, V.2
  • 20
    • 0000012317 scopus 로고    scopus 로고
    • Towards optimal feature selection
    • Koller D, Sahami M. Towards optimal feature selection. In: ICML, vol. 96; 1996. p. 284-92.
    • (1996) ICML , vol.96 , pp. 284-292
    • Koller, D.1    Sahami, M.2
  • 23
    • 84856505051 scopus 로고    scopus 로고
    • Feature selection based on class-dependent densities for high-dimensional binary data
    • K. Javed, H.A. Babri, and M. Saeed Feature selection based on class-dependent densities for high-dimensional binary data IEEE Trans Knowl Data Eng 24 2010
    • (2010) IEEE Trans Knowl Data Eng , vol.24
    • Javed, K.1    Babri, H.A.2    Saeed, M.3
  • 24
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy
    • H. Peng, F. Long, and C. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans Pattern Anal Mach Intell 27 2005
    • (2005) IEEE Trans Pattern Anal Mach Intell , vol.27
    • Peng, H.1    Long, F.2    Ding, C.3
  • 26
    • 84857553754 scopus 로고    scopus 로고
    • A comparison of feature selection procedures for classifier based on kernel density estimation
    • E. Acuna, F. Coaquira, and M. Gonzalez A comparison of feature selection procedures for classifier based on kernel density estimation Proc Comput Commun Control Technol 1 2003 468 472
    • (2003) Proc Comput Commun Control Technol , vol.1 , pp. 468-472
    • Acuna, E.1    Coaquira, F.2    Gonzalez, M.3
  • 27
    • 2942701493 scopus 로고    scopus 로고
    • Ranking a random feature for variable and feature selection
    • H. Stoppiglia, G. Dreyfus, R. Dubios, and Y. Oussar Ranking a random feature for variable and feature selection J Mach Res 3 2003 1399 1414
    • (2003) J Mach Res , vol.3 , pp. 1399-1414
    • Stoppiglia, H.1    Dreyfus, G.2    Dubios, R.3    Oussar, Y.4
  • 29
    • 77954565155 scopus 로고    scopus 로고
    • Discriminative semi-supervised feature selection via manifold regularization
    • Z. Xu, I. King, M.R.-T. Lyu, and R. Jin Discriminative semi-supervised feature selection via manifold regularization IEEE Trans Neural Networks 21 2010
    • (2010) IEEE Trans Neural Networks , vol.21
    • Xu, Z.1    King, I.2    Lyu, M.R.-T.3    Jin, R.4
  • 30
    • 0017535866 scopus 로고
    • A branch and bound algorithm for feature subset selection
    • P. Narendra, and K. Fukunaga A branch and bound algorithm for feature subset selection IEEE Trans Comput 6 1977 917 922
    • (1977) IEEE Trans Comput , vol.6 , pp. 917-922
    • Narendra, P.1    Fukunaga, K.2
  • 33
    • 0028547556 scopus 로고
    • Floating search methods in feature selection
    • P. Pudil, J. Novovicova, and J. Kittler Floating search methods in feature selection Pattern Recog Lett 15 1994 1119 1125
    • (1994) Pattern Recog Lett , vol.15 , pp. 1119-1125
    • Pudil, P.1    Novovicova, J.2    Kittler, J.3
  • 34
    • 84890445089 scopus 로고    scopus 로고
    • Overfitting in making comparisons between variable selection methods
    • J. Reunanen Overfitting in making comparisons between variable selection methods J Mach Learn Res 3 2003 1371 1382
    • (2003) J Mach Learn Res , vol.3 , pp. 1371-1382
    • Reunanen, J.1
  • 36
    • 84894904864 scopus 로고    scopus 로고
    • A comparison of feature selection methods for the detection of breast cancers in mammograms: Adaptive sequential floating search vs genetic algorithm
    • 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.
    • Conf Proc IEEE Eng Med Biol Soc , vol.6
    • Sun, Y.1    Babbs, C.2    Delp, E.3
  • 37
    • 67349133167 scopus 로고    scopus 로고
    • An improvement on floating search algorithms for feature subset selection
    • S. Nakariyakul, and D.P. Casasent An improvement on floating search algorithms for feature subset selection Pattern Recog 42 2009 1932 1940
    • (2009) Pattern Recog , vol.42 , pp. 1932-1940
    • Nakariyakul, S.1    Casasent, D.P.2
  • 39
    • 12844283500 scopus 로고    scopus 로고
    • A two-stage evolutionary algorithm for variable selection in the development of rbf neural network models
    • A. Alexandridis, P. Patrinos, H. Sarimveis, and G. Tsekouras A two-stage evolutionary algorithm for variable selection in the development of rbf neural network models Chemomet Intell Lab Syst 75 2005 149 162
    • (2005) Chemomet Intell Lab Syst , vol.75 , pp. 149-162
    • Alexandridis, A.1    Patrinos, P.2    Sarimveis, H.3    Tsekouras, G.4
  • 40
    • 0001238580 scopus 로고    scopus 로고
    • Genetic algorithms as a tool for wavenumber selection in multivariate calibration
    • Jouan-Rimbaud D, Massart DL, Leardi R, Noord OED. Genetic algorithms as a tool for wavenumber selection in multivariate calibration. Anal Chem 67.
    • Anal Chem , vol.67
    • Jouan-Rimbaud, D.1    Massart, D.L.2    Leardi, R.3    Oed, N.4
  • 41
    • 0032028297 scopus 로고    scopus 로고
    • Feature subset selection using a genetic algorithm
    • J. Yang, and V. Honavar Feature subset selection using a genetic algorithm IEEE Intell Syst Appl 13 1998 44 49
    • (1998) IEEE Intell Syst Appl , vol.13 , pp. 44-49
    • Yang, J.1    Honavar, V.2
  • 43
    • 0001334115 scopus 로고
    • The CHC adaptive search algorithm: How to have safe search when engaging in nontraditional genetic recombination
    • G.J.E. Rawlins, Morgan Kauffman
    • L. Eshelman The CHC adaptive search algorithm: how to have safe search when engaging in nontraditional genetic recombination G.J.E. Rawlins, In foundations of genetic algorithms 1991 Morgan Kauffman
    • (1991) Foundations of Genetic Algorithms
    • Eshelman, L.1
  • 44
    • 33646875753 scopus 로고    scopus 로고
    • Feature-based image registration by means of the chc evolutionary algorithm
    • O. Cordon, S. Damas, and J. Santamaria Feature-based image registration by means of the chc evolutionary algorithm Image Vis Comput 24 2006 525 533
    • (2006) Image Vis Comput , vol.24 , pp. 525-533
    • Cordon, O.1    Damas, S.2    Santamaria, J.3
  • 45
    • 0142086622 scopus 로고    scopus 로고
    • A methodology for feature selection using multiobjective genetic algorithms for handwritten digit sting recognition
    • L. Oliveira, R. Sabourin, F. Bortolozzi, and C. Suen A methodology for feature selection using multiobjective genetic algorithms for handwritten digit sting recognition Int J Pattern Recog Artif Intell 17 2003 903 929
    • (2003) Int J Pattern Recog Artif Intell , vol.17 , pp. 903-929
    • Oliveira, L.1    Sabourin, R.2    Bortolozzi, F.3    Suen, C.4
  • 46
    • 85013515810 scopus 로고
    • Comparative study of techniques for large-scale feature selection
    • F. Ferri, P. Pudil, M. Hatef, and J. Kittler Comparative study of techniques for large-scale feature selection Pattern Recog Pract 1994 403 413
    • (1994) Pattern Recog Pract , pp. 403-413
    • Ferri, F.1    Pudil, P.2    Hatef, M.3    Kittler, J.4
  • 47
    • 0033640901 scopus 로고    scopus 로고
    • Comparison of algorithms that select features for pattern classifiers
    • M. Kudo, and J. Sklansky Comparison of algorithms that select features for pattern classifiers Pattern Recog 33 2000 327 336
    • (2000) Pattern Recog , vol.33 , pp. 327-336
    • Kudo, M.1    Sklansky, J.2
  • 49
    • 63149139219 scopus 로고    scopus 로고
    • Gene selection in cancer classification using pso/svm and ga/svm hybrid algorithms
    • E. Alba, J. Garcia-Nieto, L. Jourdan, and E.-G. Talbi Gene selection in cancer classification using pso/svm and ga/svm hybrid algorithms Evol Comput 2007 284 290
    • (2007) Evol Comput , pp. 284-290
    • Alba, E.1    Garcia-Nieto, J.2    Jourdan, L.3    Talbi, E.-G.4
  • 53
    • 35348920168 scopus 로고    scopus 로고
    • Feature selection and classification of hyperspectral images with support vector machines
    • R. Archibald, and G. Fann Feature selection and classification of hyperspectral images with support vector machines IEEE Geosci Remote Sens Lett 4 2007
    • (2007) IEEE Geosci Remote Sens Lett , vol.4
    • Archibald, R.1    Fann, G.2
  • 54
    • 30044438683 scopus 로고    scopus 로고
    • Combined svm-based feature selection and classification
    • J. Neumann, C. Schnorr, and G. Steidl Combined svm-based feature selection and classification Mach Learn 61 2005 129 150
    • (2005) Mach Learn , vol.61 , pp. 129-150
    • Neumann, J.1    Schnorr, C.2    Steidl, G.3
  • 56
    • 40949143180 scopus 로고    scopus 로고
    • Performing feature selection with multilayer perceptrons
    • E. Romero, and J.M. Sopena Performing feature selection with multilayer perceptrons IEEE Trans Neural Networks 19 2008
    • (2008) IEEE Trans Neural Networks , vol.19
    • Romero, E.1    Sopena, J.M.2
  • 57
    • 33845302828 scopus 로고    scopus 로고
    • Randomized variable elimination
    • D.J. Stracuzzi, and P.E. Utgoff Randomized variable elimination J Mach Learn 5 2004 1331 1364
    • (2004) J Mach Learn , vol.5 , pp. 1331-1364
    • Stracuzzi, D.J.1    Utgoff, P.E.2
  • 58
    • 73849129973 scopus 로고    scopus 로고
    • Uninformation variable elimination and successive projections algorithm in mid-infrared spectra wavenumber selection
    • D. Wu, Z. Zhou, S. Feng, and Y. He Uninformation variable elimination and successive projections algorithm in mid-infrared spectra wavenumber selection Image Signal Process 2009
    • (2009) Image Signal Process
    • Wu, D.1    Zhou, Z.2    Feng, S.3    He, Y.4
  • 61
    • 77957565222 scopus 로고    scopus 로고
    • Lazy learner text categorization algorithm based on embedded feature selection
    • Y. Peng, Z. Xuefeng, Z. Jianyong, and X. Yunhong Lazy learner text categorization algorithm based on embedded feature selection J Syst Eng Electron 20 2009 651 659
    • (2009) J Syst Eng Electron , vol.20 , pp. 651-659
    • Peng, Y.1    Xuefeng, Z.2    Jianyong, Z.3    Yunhong, X.4
  • 63
    • 0037965523 scopus 로고
    • Feature selection based on the approximation of class densities by finite mixtures of the special type
    • P. Pudil, J. Novovicova, and J. Kittler Feature selection based on the approximation of class densities by finite mixtures of the special type Pattern Recog 28 1995 1389 1398
    • (1995) Pattern Recog , vol.28 , pp. 1389-1398
    • Pudil, P.1    Novovicova, J.2    Kittler, J.3
  • 65
  • 66
    • 33745456231 scopus 로고    scopus 로고
    • Tech rep 1530, computer sciences University of Wisconsin-Madison
    • X. Zhu Semi-supervised learning literature survey Tech rep 1530, computer sciences 2005 University of Wisconsin-Madison
    • (2005) Semi-supervised Learning Literature Survey
    • Zhu, X.1
  • 67
    • 70449102559 scopus 로고    scopus 로고
    • Semi-supervised feature selection via spectral analysis
    • Zhao Z, Liu H. Semi-supervised feature selection via spectral analysis. In: Proc 7th SIAM data mining conf (SDM); 2007. p. 641-6.
    • (2007) Proc 7th SIAM Data Mining Conf (SDM) , pp. 641-646
    • Zhao, Z.1    Liu, H.2
  • 68
    • 83755163963 scopus 로고    scopus 로고
    • The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures
    • A.-C. Haury, P. Gestraud, and J.-P. Vert The influence of feature selection methods on accuracy, stability and interpretability of molecular signatures PLoS ONE 6 2011 e28210
    • (2011) PLoS ONE , vol.6 , pp. 28210
    • Haury, A.-C.1    Gestraud, P.2    Vert, J.-P.3
  • 69
    • 77949507309 scopus 로고    scopus 로고
    • Robust biomarker identification for cancer diagnosis with ensemble feature selection methods
    • A. T, H. T, V. de Peer Y, D. P, and S. Y Robust biomarker identification for cancer diagnosis with ensemble feature selection methods Bioinformatics 26 2010 392 398
    • (2010) Bioinformatics , vol.26 , pp. 392-398
    • Ai, T.1    Hu, T.2    De Peer, Y.V.3    Du, P.4    Si, Y.5
  • 71
    • 34248647608 scopus 로고    scopus 로고
    • Stability of feature selection algorithms: A study on high dimensional spaces
    • A. Kalousis, J. Prados, and M. Hilario Stability of feature selection algorithms: a study on high dimensional spaces Knowl Inform Syst 2 2007 95 116
    • (2007) Knowl Inform Syst , vol.2 , pp. 95-116
    • Kalousis, A.1    Prados, J.2    Hilario, M.3
  • 72
    • 78149286082 scopus 로고    scopus 로고
    • Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality
    • P. Somol, and J. Novovicova Evaluating stability and comparing output of feature selectors that optimize feature subset cardinality IEEE Trans Pattern Anal Mach Intell 32 2010 1921 1939
    • (2010) IEEE Trans Pattern Anal Mach Intell , vol.32 , pp. 1921-1939
    • Somol, P.1    Novovicova, J.2
  • 73
    • 79957606714 scopus 로고    scopus 로고
    • Robust feature selection for microarray data based on multicriterion fusion
    • F. Yang, and K. Mao Robust feature selection for microarray data based on multicriterion fusion IEEE/ACM Trans Comput Biol Bioinform 8 2011
    • (2011) IEEE/ACM Trans Comput Biol Bioinform , vol.8
    • Yang, F.1    Mao, K.2
  • 74
    • 0031361611 scopus 로고    scopus 로고
    • Machine learning research: Four current directions
    • T. Dietterich Machine learning research: four current directions Artif Intell Mag 18 1997 97 136
    • (1997) Artif Intell Mag , vol.18 , pp. 97-136
    • Dietterich, T.1
  • 76
    • 56749117943 scopus 로고    scopus 로고
    • In defense of one-vs-all classification
    • R. Rifkin, and A. Klautau In defense of one-vs-all classification J Mach Learn Res 5 2004 101 141
    • (2004) J Mach Learn Res , vol.5 , pp. 101-141
    • Rifkin, R.1    Klautau, A.2
  • 79
    • 59649130080 scopus 로고    scopus 로고
    • Criterion in selecting the clustering algorithm in radial basis functional link nets
    • A.S. Loong, O.H. Choon, and L.H. Chin Criterion in selecting the clustering algorithm in radial basis functional link nets WSEAS Trans Syst 7 2008 1290 1299
    • (2008) WSEAS Trans Syst , vol.7 , pp. 1290-1299
    • Loong, A.S.1    Choon, O.H.2    Chin, L.H.3
  • 80
    • 41149089754 scopus 로고    scopus 로고
    • Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry
    • J.V. Marcos, R. Hornero, and D. Alvarez Radial basis function classifiers to help in the diagnosis of the obstructive sleep apnoea syndrome from nocturnal oximetry Med Biol Eng Comput 46 2008 323 332
    • (2008) Med Biol Eng Comput , vol.46 , pp. 323-332
    • Marcos, J.V.1    Hornero, R.2    Alvarez, D.3
  • 81
    • 77950663983 scopus 로고    scopus 로고
    • The application of dynamic k-means clustering algorithm in the center selection of rbf neural networks
    • 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.
    • (2009) Proc 3rd International Conference on Genetic and Evolutionary Computing , vol.177 , pp. 488-491
    • Hongyang, L.1    He, J.2
  • 82
    • 84894906452 scopus 로고    scopus 로고
    • http://archive.ics.uci.edu/ml/.
  • 83
    • 84872406914 scopus 로고    scopus 로고
    • In-vivo fault prediction for rf generators using variable elimination and state-of-theart classifiers
    • October 14-17, COEX, Seoul, Korea
    • 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.
    • (2012) 2012 IEEE International Conference on Systems, Man, and Cybernetics
    • Chandrashekar, G.1    Sahin, F.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.