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Volumn 42, Issue 22, 2015, Pages 8520-8532

Feature selection using Joint Mutual Information Maximisation

Author keywords

Classification; Conditional mutual information; Dimensionality reduction; Feature selection; Feature selection stability; Joint mutual information; Mutual information; Subset feature selection

Indexed keywords

ARTIFICIAL INTELLIGENCE; BIOINFORMATICS; COMPUTATION THEORY; COMPUTATIONAL EFFICIENCY; DATA MINING; ECONOMIC AND SOCIAL EFFECTS; FEATURE EXTRACTION; IMAGE PROCESSING; INFORMATION THEORY; INFORMATION USE; INTELLIGENT SYSTEMS; LEARNING ALGORITHMS; LEARNING SYSTEMS; NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84940462014     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2015.07.007     Document Type: Article
Times cited : (575)

References (48)
  • 1
    • 84886567160 scopus 로고    scopus 로고
    • CA: University of California, School of Information and Computer Science.
    • Bache, K., & Lichman, M. (2013). UCI machine learning repository. Irvine, CA: University of California, School of Information and Computer Science. (http://archive.ics.uci.edu/ml).
    • (2013) UCI Machine Learning Repository. Irvine
    • Bache, K.1    Lichman, M.2
  • 3
    • 0028468293 scopus 로고
    • Using mutual information for selecting features in supervised neural net learning
    • Battiti R. Using mutual information for selecting features in supervised neural net learning IEEE Transactions on Neural Networks 5 1994 537 550
    • (1994) IEEE Transactions on Neural Networks , vol.5 , pp. 537-550
    • Battiti, R.1
  • 5
    • 84863403768 scopus 로고    scopus 로고
    • Conditional likelihood maximisation: A unifying framework for information theoretic feature selection
    • Brown G., Pocock A., Zhao M., Lujan M. Conditional likelihood maximisation: a unifying framework for information theoretic feature selection Journal of Machine Learning Research 13 2012 27 66
    • (2012) Journal of Machine Learning Research , vol.13 , pp. 27-66
    • Brown, G.1    Pocock, A.2    Zhao, M.3    Lujan, M.4
  • 14
    • 33645690579 scopus 로고    scopus 로고
    • Fast binary feature selection with conditional mutual information
    • Fleuret F. 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
    • Fleuret, F.1
  • 15
    • 84921784324 scopus 로고    scopus 로고
    • An evaluation of classifier-specific filter measure performance for feature selection
    • Freeman C., Kulić D., Basir O. An evaluation of classifier-specific filter measure performance for feature selection Pattern Recognition 48 2015 1812 1826
    • (2015) Pattern Recognition , vol.48 , pp. 1812-1826
    • Freeman, C.1    Kulić, D.2    Basir, O.3
  • 20
    • 9444253133 scopus 로고    scopus 로고
    • (M.S.c thesis) Computer and Information Science University of Ljubljana.
    • Jakulin, A. (2003). Attribute interactions in machine learning. (M.S.c thesis), Computer and Information Science, University of Ljubljana.
    • (2003) Attribute Interactions in Machine Learning.
    • Jakulin, A.1
  • 21
  • 25
    • 0002719797 scopus 로고
    • The Hungarian method for the assignment problem
    • Kuhn H. The Hungarian method for the assignment problem Naval Research Logistic Quarterly 2 1955 83 97
    • (1955) Naval Research Logistic Quarterly , vol.2 , pp. 83-97
    • Kuhn, H.1
  • 27
    • 0036127473 scopus 로고    scopus 로고
    • Input feature selection for classification problems
    • Kwok N., Choi C. Input feature selection for classification problems IEEE Transactions on Neural Networks 13 2002 143 159
    • (2002) IEEE Transactions on Neural Networks , vol.13 , pp. 143-159
    • Kwok, N.1    Choi, C.2
  • 28
    • 84929510253 scopus 로고    scopus 로고
    • Fast multi-label feature selection based on information-theoretic feature ranking
    • Lee J., Kim D. Fast multi-label feature selection based on information-theoretic feature ranking Pattern Recognition 48 2015 2761 2771
    • (2015) Pattern Recognition , vol.48 , pp. 2761-2771
    • Lee, J.1    Kim, D.2
  • 30
    • 1842679412 scopus 로고    scopus 로고
    • Implementing the fisher's discriminant ratio in a k-means clustering algorithm for feature selection and dataset trimming
    • Lin T., Li H., Tsai K. Implementing the fisher's discriminant ratio in a k-means clustering algorithm for feature selection and dataset trimming Journal of Chemical Information and Computer Sciences 44 2004 76 87
    • (2004) Journal of Chemical Information and Computer Sciences , vol.44 , pp. 76-87
    • Lin, T.1    Li, H.2    Tsai, K.3
  • 32
    • 17044405923 scopus 로고    scopus 로고
    • Toward integrating feature selection algorithms for classification and clustering
    • Liu H., Yu L. Toward integrating feature selection algorithms for classification and clustering IEEE Transactions on Knowledge and Data Engineering 17 2005 491 502
    • (2005) IEEE Transactions on Knowledge and Data Engineering , vol.17 , pp. 491-502
    • Liu, H.1    Yu, L.2
  • 35
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy
    • Peng H., Long F., Ding C. Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Transactions on Pattern Analysis and Machine Intelligence 27 2005 1226 1238
    • (2005) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.27 , pp. 1226-1238
    • Peng, H.1    Long, F.2    Ding, C.3
  • 36
    • 84952503562 scopus 로고
    • Thirteen ways to look at the correlation coefficient
    • Rodgers J., Nicewander W.A. Thirteen ways to look at the correlation coefficient The American Statistician 42 1988 59 66
    • (1988) The American Statistician , vol.42 , pp. 59-66
    • Rodgers, J.1    Nicewander, W.A.2
  • 38
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Saeys Y., Inza I., Larranaga P. A review of feature selection techniques in bioinformatics Bioinformatics 23 2007 2507 2517
    • (2007) Bioinformatics , vol.23 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larranaga, P.3
  • 39
  • 41
    • 84891840571 scopus 로고    scopus 로고
    • A review of feature selection methods based on mutual information
    • Vergara J., Estévez P. A review of feature selection methods based on mutual information Neural Computing and Applications 24 2014 175 186
    • (2014) Neural Computing and Applications , vol.24 , pp. 175-186
    • Vergara, J.1    Estévez, P.2
  • 44
    • 0001765951 scopus 로고    scopus 로고
    • A direct LDA algorithm for high-dimensional data with application to face recognition
    • Yu H., Yang J. A direct LDA algorithm for high-dimensional data with application to face recognition Pattern Recognition 34 2001 2067 2070
    • (2001) Pattern Recognition , vol.34 , pp. 2067-2070
    • Yu, H.1    Yang, J.2
  • 45
    • 25144492516 scopus 로고    scopus 로고
    • Efficient feature selection via analysis of relevance and redundancy
    • Yu L., Liu H. 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
  • 48
    • 84931575672 scopus 로고    scopus 로고
    • Feature selection for classification with class-separability strategy and data envelopment analysis
    • Zhang Y., Yang C., Yang A., Xiong C.Y., Zhou X., Zhang Z. Feature selection for classification with class-separability strategy and data envelopment analysis Neurocomputing 166 2015 172 184
    • (2015) Neurocomputing , vol.166 , pp. 172-184
    • Zhang, Y.1    Yang, C.2    Yang, A.3    Xiong, C.Y.4    Zhou, X.5    Zhang, Z.6


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.