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Volumn , Issue , 2014, Pages 3684-3689

A hybrid feature selection approach by correlation-based filters and SVM-RFE

Author keywords

Correlation based filters; Feature selection; Multiple groups; SVM RFE

Indexed keywords

CLUSTERING ALGORITHMS; SUPPORT VECTOR MACHINES; TEXT PROCESSING;

EID: 84919918822     PISSN: 10514651     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICPR.2014.633     Document Type: Conference Paper
Times cited : (12)

References (18)
  • 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," Journal of Machine Learning Research, vol. 3, pp. 1157-1182, 2003
    • (2003) Journal of Machine Learning Research , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 3
    • 77952717202 scopus 로고    scopus 로고
    • Sparse representation for computer vision and pattern recognition
    • J. Wright, Y. Ma, J. Mairal, G. Sapiro, T. S. Huang, and S. Yan. "Sparse representation for computer vision and pattern recognition," Proceedings of the IEEE, vol. 98, no. 6, pp. 10311044, 2010
    • (2010) Proceedings of the IEEE , vol.98 , Issue.6 , pp. 10311044
    • Wright, J.1    Ma, Y.2    Mairal, J.3    Sapiro, G.4    Huang, T.S.5    Yan, S.6
  • 5
    • 84864145739 scopus 로고    scopus 로고
    • Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification
    • S. L. Wang, X. L. Li, and J. W. Fang, "Finding minimum gene subsets with heuristic breadth-first search algorithm for robust tumor classification," BMC Bioinformatics, vol. 13, pp. 178, 2012
    • (2012) BMC Bioinformatics , vol.13 , pp. 178
    • Wang, S.L.1    Li, X.L.2    Fang, J.W.3
  • 6
    • 84873170469 scopus 로고    scopus 로고
    • Maximum weight and minimum redundancy: A novel framework for feature subset selection
    • J. Z. Wang, L. S. Wu, J. Kong, Y. X. Li, and B. X. Zhang, "Maximum weight and minimum redundancy: A novel framework for feature subset selection," Pattern Recognition, vol. 46, no. 6, pp. 1616-1627, 2013
    • (2013) Pattern Recognition , vol.46 , Issue.6 , pp. 1616-1627
    • Wang, J.Z.1    Wu, L.S.2    Kong, J.3    Li, Y.X.4    Zhang, B.X.5
  • 7
    • 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," The Journal of Machine Learning Research, vol. 5, pp. 1205-1224, 2004
    • (2004) The Journal of Machine Learning Research , vol.5 , pp. 1205-1224
    • Yu, L.1    Liu, H.2
  • 8
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy
    • H. C. Peng, F. H. 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, vol. 27, no. 8, pp. 1226-1238, 2005
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , Issue.8 , pp. 1226-1238
    • Peng, H.C.1    Long, F.H.2    Ding, C.3
  • 9
    • 0036139278 scopus 로고    scopus 로고
    • Gene selection for sample classification based on gene expression data: Study of sensitivity to choice of parameters of the GA/KNN method
    • L. Li, C. R. Weinberg, T. A. Darden, and L. G. Pedersen, "Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method," Bioinformatics, vol. 17, no. 12, pp: 1131-1142, 2001
    • (2001) Bioinformatics , vol.17 , Issue.12 , pp. 1131-1142
    • Li, L.1    Weinberg, C.R.2    Darden, T.A.3    Pedersen, L.G.4
  • 10
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Machine learning, vol. 46, no. 1-3, pp: 389-422, 2002
    • (2002) Machine Learning , vol.46 , Issue.1-3 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 11
    • 17444386734 scopus 로고    scopus 로고
    • HykGene: A hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data
    • Y. Wang, F. S. Makedon, J. C. Ford, and J. Pearlman, "HykGene: A hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data," Bioinformatics, vol. 21, no. 8, pp: 1530-1537, 2005
    • (2005) Bioinformatics , vol.21 , Issue.8 , pp. 1530-1537
    • Wang, Y.1    Makedon, F.S.2    Ford, J.C.3    Pearlman, J.4
  • 13
    • 84855461005 scopus 로고    scopus 로고
    • A hybrid BPSO-CGA approach for gene selection and classification of microarray data
    • L. Y. Chuang, C. H. Yang, J. C. Li, and C. H. Yang, "A hybrid BPSO-CGA approach for gene selection and classification of microarray data," Journal of Computational Biology, vol. 19, no. 1, pp: 68-82, 2012
    • (2012) Journal of Computational Biology , vol.19 , Issue.1 , pp. 68-82
    • Chuang, L.Y.1    Yang, C.H.2    Li, J.C.3    Yang, C.H.4
  • 14
    • 74649083315 scopus 로고    scopus 로고
    • Ensemble gene selection by grouping for microarray data classification
    • H. W. Liu, L. Liu, and H. J. Zhang, "Ensemble gene selection by grouping for microarray data classification," Journal of Biomedical Informatics, vol. 43, no. 1, pp: 81-87, 2010
    • (2010) Journal of Biomedical Informatics , vol.43 , Issue.1 , pp. 81-87
    • Liu, H.W.1    Liu, L.2    Zhang, H.J.3
  • 15
    • 84863403768 scopus 로고    scopus 로고
    • Conditional likelihood maximisation: A unifying framework for information theoretic feature selection
    • G. Brown, A. Pocock, M. J. Zhao, and M. Lujan, "Conditional likelihood maximisation: A unifying framework for information theoretic feature selection," The Journal of Machine Learning Research, vol. 13, pp: 27-66, 2012
    • (2012) The Journal of Machine Learning Research , vol.13 , pp. 27-66
    • Brown, G.1    Pocock, A.2    Zhao, M.J.3    Lujan, M.4
  • 16
    • 79955444979 scopus 로고    scopus 로고
    • The Fisher-Markov selector: Fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data
    • Q. Cheng, H. B. Zhou, and J. Cheng. "The Fisher-Markov selector: fast selecting maximally separable feature subset for multiclass classification with applications to high-dimensional data," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 33, no. 6, pp. 1217-1233, 2011
    • (2011) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.33 , Issue.6 , pp. 1217-1233
    • Cheng, Q.1    Zhou, H.B.2    Cheng, J.3
  • 18
    • 0003957032 scopus 로고    scopus 로고
    • Weka: Practical machine learning tools and techniques with java implementations
    • New Zealand: University of Waikato, Department of Computer Science
    • I. H. Witten, E. Frank, L. E. Trigg, M. A. Mark, G. Holmes, and S. J. Cunningham, "Weka: Practical Machine Learning Tools and Techniques with Java Implementations," Hamilton, New Zealand: University of Waikato, Department of Computer Science, 1999.
    • (1999) Hamilton
    • Witten, I.H.1    Frank, E.2    Trigg, L.E.3    Mark, M.A.4    Holmes, G.5    Cunningham, S.J.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.