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Volumn 39, Issue 5, 2012, Pages 6078-6088

Feature subset selection with cumulate conditional mutual information minimization

Author keywords

Classification; Conditional mutual information; Feature selection; Redundancy; Relevance

Indexed keywords

CONDITIONAL MUTUAL INFORMATION; DATA SETS; EMPIRICAL RESULTS; FAST IMPLEMENTATION; FEATURE SELECTION ALGORITHM; FEATURE SUBSET SELECTION; MINIMAL INFORMATION; MUTUAL INFORMATIONS; REDUNDANT FEATURES; RELEVANCE;

EID: 84855872857     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.12.003     Document Type: Article
Times cited : (47)

References (46)
  • 1
    • 0025725905 scopus 로고
    • Instance-based learning algorithms
    • D. Aha, and D. Kibler Instance-based learning algorithms Machine Learning 6 1991 37 66
    • (1991) Machine Learning , vol.6 , pp. 37-66
    • Aha, D.1    Kibler, D.2
  • 2
    • 0004493166 scopus 로고    scopus 로고
    • On the approximation of minimizing non zero variables or unsatisfied relations in linear systems
    • E. Amaldi, and V. Kann On the approximation of minimizing non zero variables or unsatisfied relations in linear systems Theoretical Computer Science 209 1-2 1998 237 260
    • (1998) Theoretical Computer Science , vol.209 , Issue.12 , pp. 237-260
    • Amaldi, E.1    Kann, V.2
  • 3
    • 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 Transactions on Neural Networks 5 4 1994 537 550
    • (1994) IEEE Transactions on Neural Networks , vol.5 , Issue.4 , pp. 537-550
    • Battiti, R.1
  • 5
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • A. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1997 245 271
    • (1997) Artificial Intelligence , vol.97 , pp. 245-271
    • Blum, A.1    Langley, P.2
  • 8
    • 0013326060 scopus 로고    scopus 로고
    • Feature selection for classification
    • M. Dash, and H. Liu Feature selection for classification Intelligent Data Analysis 1 1997 131 156
    • (1997) Intelligent Data Analysis , vol.1 , pp. 131-156
    • Dash, M.1    Liu, H.2
  • 9
    • 84960463485 scopus 로고    scopus 로고
    • Minimum redundancy feature selection from microarray gene expression data
    • IEEE computer society Washington, DC, USA
    • Ding, C.; & Peng, H. (2003). Minimum redundancy feature selection from microarray gene expression data. In: Proceedings of the IEEE computer society conference on bioinformatics. CSB'03. IEEE computer society (pp. 523-528). Washington, DC, USA.
    • (2003) Proceedings of the IEEE Computer Society Conference on Bioinformatics. CSB'03 , pp. 523-528
    • Ding, C.1    Peng, H.2
  • 11
    • 33645690579 scopus 로고    scopus 로고
    • Fast binary feature selection with conditional mutual information
    • F. Flueret Fast binary feature selection with conditional mutual information Journal of Machine Learning Research 5 2004 1531 1555
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1531-1555
    • Flueret, F.1
  • 12
    • 0033636139 scopus 로고    scopus 로고
    • Support vector machine classification and validation of cancer tissue samples using microarray expression data
    • T.S. Furey, N. Cristianini, N. Duffy, D.W. Bednarski, M. Schummer, and D. Haussler Support vector machine classification and validation of cancer tissue samples using microarray expression data Bioinformatics 16 10 2000 906 914
    • (2000) Bioinformatics , vol.16 , Issue.10 , pp. 906-914
    • Furey, T.S.1    Cristianini, N.2    Duffy, N.3    Bednarski, D.W.4    Schummer, M.5    Haussler, D.6
  • 14
    • 85065703189 scopus 로고    scopus 로고
    • Correlation-based feature selection for discrete and numeric class machine learning
    • Morgan Kaufmann, Los Altos, CA, USA
    • Hall, M.A. (2000). Correlation-based feature selection for discrete and numeric class machine learning. In: Proceedings of the seventh international conference on machine learning. ICML'00 (pp. 359-366). Morgan Kaufmann, Los Altos, CA, USA.
    • (2000) Proceedings of the Seventh International Conference on Machine Learning. ICML'00 , pp. 359-366
    • Hall, A.M.1
  • 15
    • 12144251725 scopus 로고    scopus 로고
    • Effective feature selection scheme using mutual information
    • D. Huang, and T.W.S. Chow Effective feature selection scheme using mutual information Neurocomputing 63 2005 325 343
    • (2005) Neurocomputing , vol.63 , pp. 325-343
    • Huang, D.1    Chow, T.W.S.2
  • 16
    • 40649115462 scopus 로고    scopus 로고
    • A parameterless feature ranking algorithm based on mi
    • J.J. Huang, Y.Z. Cai, and X.M. Xu A parameterless feature ranking algorithm based on mi Neurocomputing 71 2008 1656 1668
    • (2008) Neurocomputing , vol.71 , pp. 1656-1668
    • Huang, J.J.1    Cai, Y.Z.2    Xu, X.M.3
  • 20
    • 84992726552 scopus 로고
    • Estimating attributes: Analysis and extensions of relief
    • Secaucus, NJ, USA: Springer-Verlag New York, Inc.
    • Kononenko, I. (1994). Estimating attributes: analysis and extensions of relief. In: Proceedings of European Conference on Machine Learning. ECML'94 (pp. 171-182). Secaucus, NJ, USA: Springer-Verlag New York, Inc.
    • (1994) Proceedings of European Conference on Machine Learning. ECML'94 , pp. 171-182
    • Kononenko, I.1
  • 21
    • 0036127473 scopus 로고    scopus 로고
    • Input feature selection for classification problems
    • N. Kwak, and C.H. Choi Input feature selection for classification problems IEEE Transactions on Neural Networks 13 1 2002 143 159
    • (2002) IEEE Transactions on Neural Networks , vol.13 , Issue.1 , pp. 143-159
    • Kwak, N.1    Choi, C.H.2
  • 22
    • 17044405923 scopus 로고    scopus 로고
    • Toward integrating features election algorithms for classification and clustering
    • H. Liu, and L. Yu Toward integrating features election algorithms for classification and clustering IEEE Transactions on Knowledge and Data Engineering 17 4 2005 491 502
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , Issue.4 , pp. 491-502
    • Liu, H.1    Yu, L.2
  • 26
    • 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 Transactions on Pattern Analysis and Machine Intelligence 27 8 2005 1226 1238
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3
  • 28
    • 85115260483 scopus 로고
    • Floating search methods for feature selection with nonmonotonic criterion functions
    • P. Pudil, F.J. Ferri, J. Novovicova, and J. Kittler Floating search methods for feature selection with nonmonotonic criterion functions Pattern Recognition 2 1994 279 283
    • (1994) Pattern Recognition , vol.2 , pp. 279-283
    • Pudil, P.1    Ferri, F.J.2    Novovicova, J.3    Kittler, J.4
  • 31
    • 0141990695 scopus 로고    scopus 로고
    • Theoretical and empirical analysis of relief and relieff
    • M. Robnik-Sikonja, and I. Kononenko Theoretical and empirical analysis of relief and relieff Machine Learning 53 2003 23 69
    • (2003) Machine Learning , vol.53 , pp. 23-69
    • Robnik-Sikonja, M.1    Kononenko, I.2
  • 34
    • 76749129275 scopus 로고    scopus 로고
    • Supervised feature selection by clustering using conditional mutual information-based distances
    • J.M. Sotoca, and F. Pla Supervised feature selection by clustering using conditional mutual information-based distances Pattern Recognition 43 6 2010 2068 2081
    • (2010) Pattern Recognition , vol.43 , Issue.6 , pp. 2068-2081
    • Sotoca, J.M.1    Pla, F.2
  • 35
    • 1942450610 scopus 로고    scopus 로고
    • Feature extraction by non-parametric mutual information maximization
    • K. Torkkola Feature extraction by non-parametric mutual information maximization Journal of Machine Learning Research 3 2003 1415 1438
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1415-1438
    • Torkkola, K.1
  • 36
    • 76749137632 scopus 로고    scopus 로고
    • Local causal and markov blanket induction for causal discovery and feature selection for classification part i: Algorithms and empirical evaluation
    • I. Tsamardinos, C. Aliferis, and A. Statnikov Local causal and markov blanket induction for causal discovery and feature selection for classification part i: Algorithms and empirical evaluation Journal of Machine Learning Research 11 2010 171 234
    • (2010) Journal of Machine Learning Research , vol.11 , pp. 171-234
    • Tsamardinos, I.1    Aliferis, C.2    Statnikov, A.3
  • 38
    • 33746035971 scopus 로고    scopus 로고
    • The max-min hillclimbing bayesian network structure learning algorithm
    • I. Tsamardinos, L.E. Brown, and C.F. Aliferis The max-min hillclimbing bayesian network structure learning algorithm Machine Learning 65 1 2006 31 78
    • (2006) Machine Learning , vol.65 , Issue.1 , pp. 31-78
    • Tsamardinos, I.1    Brown, L.E.2    Aliferis, C.F.3
  • 40
    • 0038797944 scopus 로고    scopus 로고
    • 2nd ed. John Wiley & Sons Ltd Chichester, West Sussex, England
    • A.R. Webb Statistical pattern recognition 2nd ed. 2002 John Wiley & Sons Ltd Chichester, West Sussex, England
    • (2002) Statistical Pattern Recognition
    • Webb, A.R.1
  • 45
    • 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 Journal of Machine Learning Research 5 2004 1205 1224
    • (2004) Journal of Machine Learning Research , vol.5 , pp. 1205-1224
    • Yu, L.1    Liu, H.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.