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Volumn 326, Issue , 2016, Pages 102-118

High-dimensional feature selection via feature grouping: A Variable Neighborhood Search approach

Author keywords

Feature grouping; Feature selection; High dimensionality; Metaheuristic

Indexed keywords

CLUSTERING ALGORITHMS; DATA MINING; OPTIMIZATION;

EID: 84943748390     PISSN: 00200255     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ins.2015.07.041     Document Type: Article
Times cited : (123)

References (79)
  • 1
    • 0033536012 scopus 로고    scopus 로고
    • Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays
    • U. Alon, N. Barkai, D.A. Notterman, K. Gishdagger, S. Ybarradagger, D. Mackdagger, and A.J. Levine Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays Proc. Natl. Acad. Sci. USA 96 12 1999 6745 6750
    • (1999) Proc. Natl. Acad. Sci. USA , vol.96 , Issue.12 , pp. 6745-6750
    • Alon, U.1    Barkai, N.2    Notterman, D.A.3    Gishdagger, K.4    Ybarradagger, S.5    Mackdagger, D.6    Levine, A.J.7
  • 2
    • 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 1183 1208
    • (2003) J. Mach. Learn Res. , vol.3 , pp. 1183-1208
    • Bekkerman, R.1    El-Yaniv, R.2    Tishby, N.3    Winter, Y.4
  • 3
    • 0034324043 scopus 로고    scopus 로고
    • A formalism for relevance and its application in feature subset selection
    • D. Bell, and H. Wang A formalism for relevance and its application in feature subset selection Mach Learn 41 2 2000 175 195
    • (2000) Mach Learn , vol.41 , Issue.2 , pp. 175-195
    • Bell, D.1    Wang, H.2
  • 5
    • 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 1-2 1997 245 271
    • (1997) Artif. Intell. , vol.97 , Issue.1-2 , pp. 245-271
    • Blum, A.L.1    Langley, P.2
  • 6
    • 0026453958 scopus 로고
    • Training a 3-node neural networks is NP-complete
    • A.L. Blum, and R.L. Rivest Training a 3-node neural networks is NP-complete Neural Netw. 5 1992 117 127
    • (1992) Neural Netw. , vol.5 , pp. 117-127
    • Blum, A.L.1    Rivest, R.L.2
  • 9
    • 84894903349 scopus 로고    scopus 로고
    • A survey on feature selection methods
    • G. Chandrashekar, and F. Sahin A survey on feature selection methods Comput. Electr. Eng. 40 1 2014 16 28
    • (2014) Comput. Electr. Eng. , vol.40 , Issue.1 , pp. 16-28
    • Chandrashekar, G.1    Sahin, F.2
  • 13
    • 0013326060 scopus 로고    scopus 로고
    • Feature selection for classification
    • M. Dash, and H. Liu Feature selection for classification Intell. Data Anal. 1 1997 131 156
    • (1997) Intell. Data Anal. , vol.1 , pp. 131-156
    • Dash, M.1    Liu, H.2
  • 14
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • J. Demšar Statistical comparisons of classifiers over multiple data sets J. Mach. Learn. Res. 7 2006 1 30
    • (2006) J. Mach. Learn. Res. , vol.7 , pp. 1-30
    • Demšar, J.1
  • 15
    • 0037709918 scopus 로고    scopus 로고
    • Supervised clustering of genes
    • M. Dettling, and P. Buhlmann Supervised clustering of genes Genome Biol. 3 12 2002 0069.1 0069.15
    • (2002) Genome Biol. , vol.3 , Issue.12 , pp. 00691-006915
    • Dettling, M.1    Buhlmann, P.2
  • 16
    • 4143134783 scopus 로고    scopus 로고
    • Finding predictive gene groups from microarray data
    • M. Dettling, and P. Buhlmann Finding predictive gene groups from microarray data J. Multivar. Anal. 90 1 2004 106 131
    • (2004) J. Multivar. Anal. , vol.90 , Issue.1 , pp. 106-131
    • Dettling, M.1    Buhlmann, P.2
  • 17
    • 2942723846 scopus 로고    scopus 로고
    • A divisive information theoretic feature clustering algorithm for text classification
    • I.S. Dhillon, S. Mallela, and R. Kumar A divisive information theoretic feature clustering algorithm for text classification J. Mach. Learn. Res. 3 2003 1265 1287
    • (2003) J. Mach. Learn. Res. , vol.3 , pp. 1265-1287
    • Dhillon, I.S.1    Mallela, S.2    Kumar, R.3
  • 18
    • 17644384367 scopus 로고    scopus 로고
    • Minimum redundancy feature selection from microarray gene expression data
    • C.H.Q. Ding, and H. Peng Minimum redundancy feature selection from microarray gene expression data J. Bioinform. Comput. Biol. 3 2 2005 185 206
    • (2005) J. Bioinform. Comput. Biol. , vol.3 , Issue.2 , pp. 185-206
    • Ding, C.H.Q.1    Peng, H.2
  • 19
    • 77949352853 scopus 로고    scopus 로고
    • A selective overview of variable selection in high dimensional feature space
    • J. Fan, and J. Lv A selective overview of variable selection in high dimensional feature space Stat. Sin. 20 2010 101 148
    • (2010) Stat. Sin. , vol.20 , pp. 101-148
    • Fan, J.1    Lv, J.2
  • 20
    • 84870066439 scopus 로고    scopus 로고
    • Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data
    • M. García-Torres, R. Armañanzas, C. Bielza, and P. Larrañaga Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data Inf. Sci. 222 2013 229 246
    • (2013) Inf. Sci. , vol.222 , pp. 229-246
    • García-Torres, M.1    Armañanzas, R.2    Bielza, C.3    Larrañaga, P.4
  • 21
  • 23
    • 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
  • 26
    • 84904605824 scopus 로고    scopus 로고
    • Feature subset selection by gravitational search algorithm optimization
    • X. Han, X. Chang, L. Quan, X. Xiong, J. Li, Z. Zhang, and Y. Liu Feature subset selection by gravitational search algorithm optimization Inf. Sci. 281 2014 128 146
    • (2014) Inf. Sci. , vol.281 , pp. 128-146
    • Han, X.1    Chang, X.2    Quan, L.3    Xiong, X.4    Li, J.5    Zhang, Z.6    Liu, Y.7
  • 28
    • 79952454670 scopus 로고    scopus 로고
    • Hybrid feature selection by combining filters and wrappers
    • H.H. Hsu, C.W. Hsieh, and M.D. Lu Hybrid feature selection by combining filters and wrappers Expert Syst. Appl. 38 7 2011 8144 8150
    • (2011) Expert Syst. Appl. , vol.38 , Issue.7 , pp. 8144-8150
    • Hsu, H.H.1    Hsieh, C.W.2    Lu, M.D.3
  • 29
    • 49949115667 scopus 로고    scopus 로고
    • Asymptotic properties of bridge estimators in sparse high-dimensional regression models
    • J. Huang, J.L. Horowitz, and S. Ma Asymptotic properties of bridge estimators in sparse high-dimensional regression models Ann. Stat. 36 2 2008 587 613
    • (2008) Ann. Stat. , vol.36 , Issue.2 , pp. 587-613
    • Huang, J.1    Horowitz, J.L.2    Ma, S.3
  • 30
    • 25444528447 scopus 로고    scopus 로고
    • Feature selection and classification for microarray data analysis: Evolutionary methods for identifying predictive genes
    • T. Jirapech-Umpai, and S. Aitken Feature selection and classification for microarray data analysis: evolutionary methods for identifying predictive genes BMC Bioinform. 6 148 2005 1 11
    • (2005) BMC Bioinform. , vol.6 , Issue.148 , pp. 1-11
    • Jirapech-Umpai, T.1    Aitken, S.2
  • 32
    • 0037715342 scopus 로고    scopus 로고
    • Simultaneous gene clustering and subset selection for sample classification via MDL
    • R. Jörnsten, and B. Yu Simultaneous gene clustering and subset selection for sample classification via MDL Bioinformatics 19 9 2003 1100 1109
    • (2003) Bioinformatics , vol.19 , Issue.9 , pp. 1100-1109
    • Jörnsten, R.1    Yu, B.2
  • 33
    • 84859073042 scopus 로고    scopus 로고
    • Study and analyze on feature selection in text categorization for engineering domain
    • W. Junyun Study and analyze on feature selection in text categorization for engineering domain Adv. Mater. Res. 487 2012 383 386
    • (2012) Adv. Mater. Res. , vol.487 , pp. 383-386
    • Junyun, W.1
  • 34
    • 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. Inf. Syst. 12 1 2007 95 116
    • (2007) Knowl. Inf. Syst. , vol.12 , Issue.1 , pp. 95-116
    • Kalousis, A.1    Prados, J.2    Hilario, M.3
  • 35
    • 0031381525 scopus 로고    scopus 로고
    • Wrappers for feature subset selection
    • R. Kohavi, and G.H. John Wrappers for feature subset selection Artif. Intell. 97 1-2 1997 273 324
    • (1997) Artif. Intell. , vol.97 , Issue.1-2 , pp. 273-324
    • Kohavi, R.1    John, G.H.2
  • 38
    • 38149009608 scopus 로고    scopus 로고
    • Improving stability of feature selection methods
    • Lecture Notes in Computer Science Springer
    • P. Krízek, J. Kittler, and V. Hlavác Improving stability of feature selection methods Computer Analysis of Images and Patterns Lecture Notes in Computer Science 4673 2007 Springer 929 936
    • (2007) Computer Analysis of Images and Patterns , vol.4673 , pp. 929-936
    • Krízek, P.1    Kittler, J.2    Hlavác, V.3
  • 40
    • 0032895111 scopus 로고    scopus 로고
    • Selected techniques for data mining in medicine
    • N. Lavrac Selected techniques for data mining in medicine Artif. Intell. Med. 16 1999 3 23
    • (1999) Artif. Intell. Med. , vol.16 , pp. 3-23
    • Lavrac, N.1
  • 41
    • 84922621907 scopus 로고    scopus 로고
    • Memetic feature selection algorithm for multi-label classification
    • J. Lee, and D. Kim Memetic feature selection algorithm for multi-label classification Inf. Sci. 293 2015 80 96
    • (2015) Inf. Sci. , vol.293 , pp. 80-96
    • Lee, J.1    Kim, D.2
  • 42
    • 0000727041 scopus 로고
    • The characteristic selection problem in recognition systems
    • P.M. Lewis The characteristic selection problem in recognition systems. IRE Trans. Inf. Theory 8 2 1962 171 178
    • (1962) IRE Trans. Inf. Theory , vol.8 , Issue.2 , pp. 171-178
    • Lewis, P.M.1
  • 43
    • 18244369245 scopus 로고    scopus 로고
    • Simultaneous classification and feature clustering using discriminant vector quantization with applications to microarray data analysis
    • IEEE Computer Society
    • J. Li, and H. Zha Simultaneous classification and feature clustering using discriminant vector quantization with applications to microarray data analysis 1st IEEE Computer Society Bioinformatics Conference, CSB, 14-16 August 2002 2002 IEEE Computer Society 246 255 10.1109/CSB.2002.1039347
    • (2002) 1st IEEE Computer Society Bioinformatics Conference, CSB, 14-16 August 2002 , pp. 246-255
    • Li, J.1    Zha, H.2
  • 45
    • 0141688336 scopus 로고    scopus 로고
    • On issues of instance selection
    • H. Liu, and H. Motoda On issues of instance selection Data Min. Knowl. Discov. 6 2 2002 115 130
    • (2002) Data Min. Knowl. Discov. , vol.6 , Issue.2 , pp. 115-130
    • Liu, H.1    Motoda, H.2
  • 48
    • 84943796846 scopus 로고    scopus 로고
    • R. Ltd., I. Carnegie Group, Reuters-21578, 1995.
    • R. Ltd., I. Carnegie Group, Reuters-21578, 1995.
  • 49
    • 49949090353 scopus 로고    scopus 로고
    • Penalized feature selection and classification in bioinformatics
    • S. Ma, and J. Huang Penalized feature selection and classification in bioinformatics. Brief. Bioinform. 9 5 2008 392 403
    • (2008) Brief. Bioinform. , vol.9 , Issue.5 , pp. 392-403
    • Ma, S.1    Huang, J.2
  • 52
    • 33745296717 scopus 로고    scopus 로고
    • Relevance, redundancy and differential priorization in feature selection for multiclass gene expression data
    • Lecture Notes in Computer Science/Lecture Notes in Bioinformatics Springer
    • C.H. Ooi, M. Chetty, and S.W. Teng Relevance, redundancy and differential priorization in feature selection for multiclass gene expression data Proceedings of the 6th International Symposium on Biological and Medical Data Analysis Lecture Notes in Computer Science/Lecture Notes in Bioinformatics 3745 2005 Springer 367 378
    • (2005) Proceedings of the 6th International Symposium on Biological and Medical Data Analysis , vol.3745 , pp. 367-378
    • Ooi, C.H.1    Chetty, M.2    Teng, S.W.3
  • 53
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy
    • H.L. Peng, and C.F. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Trans. Pattern Anal. Mach. Intell. 27 8 2005 1226 1238
    • (2005) IEEE Trans. Pattern Anal. Mach. Intell. , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.L.1    Ding, C.F.2
  • 57
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Y. Saeys, I. Inza, and P. Larrañaga A review of feature selection techniques in bioinformatics. Bioinformatics 23 19 2007 2507 2517
    • (2007) Bioinformatics , vol.23 , Issue.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larrañaga, P.3
  • 58
    • 0036532821 scopus 로고    scopus 로고
    • A hybrid filter/wrapper approach of feature selection using information theory
    • M. Sebban, and R. Nock A hybrid filter/wrapper approach of feature selection using information theory Pattern Recogn. 35 2002 835 846
    • (2002) Pattern Recogn. , vol.35 , pp. 835-846
    • Sebban, M.1    Nock, R.2
  • 60
    • 78649427721 scopus 로고    scopus 로고
    • Grouping pursuit through a regularization solution surface
    • X. Shen, and H.-C. Huang Grouping pursuit through a regularization solution surface J. Am. Stat. Assoc. 105 490 2010 727 739
    • (2010) J. Am. Stat. Assoc. , vol.105 , Issue.490 , pp. 727-739
    • Shen, X.1    Huang, H.-C.2
  • 62
    • 78651432006 scopus 로고    scopus 로고
    • Feature clustering with self-organizing maps and an application to financial time-series for portfolio selection
    • B. Silva, and N. Marques Feature clustering with self-organizing maps and an application to financial time-series for portfolio selection Proceeding of the Sixth International Conference on Neural Computation 2010 301 309
    • (2010) Proceeding of the Sixth International Conference on Neural Computation , pp. 301-309
    • Silva, B.1    Marques, N.2
  • 65
    • 84870441851 scopus 로고    scopus 로고
    • A fast clustering-based feature subset selection algorithm for high-dimensional data
    • Q. Song, J. Ni, and G. Wang A fast clustering-based feature subset selection algorithm for high-dimensional data IEEE Trans. Knowl. Data Eng. 25 1 2013 1 14
    • (2013) IEEE Trans. Knowl. Data Eng. , vol.25 , Issue.1 , pp. 1-14
    • Song, Q.1    Ni, J.2    Wang, G.3
  • 67
    • 0001287271 scopus 로고
    • Regression shrinkage and selection via the lasso
    • R. Tibshirani Regression shrinkage and selection via the lasso J. R. Stat. Soc. Ser. B 58 1994 267 288
    • (1994) J. R. Stat. Soc. Ser. B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 69
    • 77956611003 scopus 로고    scopus 로고
    • Mr2pso: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification
    • A. Unler, A. Murat, and R.B. Chinnam mr2pso: a maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification Inf. Sci. 181 20 2011 4625 4641
    • (2011) Inf. Sci. , vol.181 , Issue.20 , pp. 4625-4641
    • Unler, A.1    Murat, A.2    Chinnam, R.B.3
  • 70
    • 80955181170 scopus 로고    scopus 로고
    • A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm
    • H. U uz A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm Knowl.-Based Syst. 24 7 2011 1024 1032
    • (2011) Knowl.-Based Syst. , vol.24 , Issue.7 , pp. 1024-1032
    • Uz, U.H.1
  • 71
    • 84891840571 scopus 로고    scopus 로고
    • A review of feature selection methods based on mutual information
    • J.R. Vergara, and P.A. Estvez A review of feature selection methods based on mutual information. Neural Comput. Appl. 24 1 2013 175 186
    • (2013) Neural Comput. Appl. , vol.24 , Issue.1 , pp. 175-186
    • Vergara, J.R.1    Estvez, P.A.2
  • 72
    • 84856729719 scopus 로고    scopus 로고
    • A novel hybrid approach of feature selection through feature clustering using microarray gene expression data
    • IEEE
    • C.M.M. Wahid, A.B.M.S. Ali, and K.S. Tickle A novel hybrid approach of feature selection through feature clustering using microarray gene expression data Proceedings of Hybrid Intelligent Systems, HIS 2011 IEEE 121 126
    • (2011) Proceedings of Hybrid Intelligent Systems, HIS , pp. 121-126
    • Wahid, C.M.M.1    Ali, A.B.M.S.2    Tickle, K.S.3
  • 73
    • 0033097744 scopus 로고    scopus 로고
    • Axiomatic approach to feature subset selection based on relevance
    • H. Wang, D. Bell, and F. Murtagh Axiomatic approach to feature subset selection based on relevance IEEE Trans. Pattern Anal. Mach. Intell. 21 3 1999 271 277
    • (1999) IEEE Trans. Pattern Anal. Mach. Intell. , vol.21 , Issue.3 , pp. 271-277
    • Wang, H.1    Bell, D.2    Murtagh, F.3
  • 74
    • 33847753038 scopus 로고    scopus 로고
    • Accurate cancer classification using expressions of very few genes
    • L. Wang, F. Chu, and W. Xie Accurate cancer classification using expressions of very few genes IEEE/ACM Trans. Comput. Biol. Bioinform. 4 1 2007 40 53
    • (2007) IEEE/ACM Trans. Comput. Biol. Bioinform. , vol.4 , Issue.1 , pp. 40-53
    • Wang, L.1    Chu, F.2    Xie, W.3
  • 75
    • 77951771109 scopus 로고    scopus 로고
    • Ig-ga: A hybrid filter/wrapper method for feature selection of microarray data
    • C.H. Yang, L.Y. Chuang, and C.H. Yang Ig-ga: a hybrid filter/wrapper method for feature selection of microarray data J. Med. Biol. Eng. 30 1 2010 23 28
    • (2010) J. Med. Biol. Eng. , vol.30 , Issue.1 , pp. 23-28
    • Yang, C.H.1    Chuang, L.Y.2    Yang, C.H.3
  • 77
    • 25144492516 scopus 로고    scopus 로고
    • Efficient feature selection via analysis of relevance and redundancy
    • L. Yu, and H. Liu Efficient feature selection via analysis of relevance and redundancy J. Mach. Learn. Res. 5 2004 1205 1224
    • (2004) J. Mach. Learn. Res. , vol.5 , pp. 1205-1224
    • Yu, L.1    Liu, H.2
  • 78
    • 33846114377 scopus 로고    scopus 로고
    • The Adaptive Lasso and Its Oracle Properties
    • H. Zou The Adaptive Lasso and Its Oracle Properties J. Am. Stat. Assoc. 101 476 2006 1418 1429
    • (2006) J. Am. Stat. Assoc. , vol.101 , Issue.476 , pp. 1418-1429
    • Zou, H.1
  • 79
    • 16244401458 scopus 로고    scopus 로고
    • Regularization and variable selection via the elastic net
    • H. Zou, and T. Hastie Regularization and variable selection via the elastic net J. R. Stat. Soc. Ser. B 67 2005 301 320
    • (2005) J. R. Stat. Soc. Ser. B , vol.67 , pp. 301-320
    • Zou, H.1    Hastie, T.2


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