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Volumn 6658 LNCS, Issue , 2011, Pages 205-214

A graph-based approach to feature selection

Author keywords

[No Author keywords available]

Indexed keywords

COMBINATORIAL OPTIMIZATION PROBLEMS; DATA ANALYSIS; DATA SETS; DOMINANT SET CLUSTERING; FEATURE INTERACTIONS; FEATURE SELECTION; FEATURE SELECTION METHODS; FEATURE SELECTION PROBLEM; GRAPH-BASED; HIGH DIMENSIONAL DATA; HIGHER ORDER; INTERACTION INFORMATION; MUTUAL INFORMATIONS; SEARCH SPACES; THREE PHASIS; MULTIDIMENSIONAL INTERACTION INFORMATIONS (MII);

EID: 79956335726     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-20844-7_21     Document Type: Article
Times cited : (43)

References (10)
  • 1
    • 0028468293 scopus 로고    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(4), 537-550 (2002)
    • (2002) IEEE Transactions on Neural. Networks , vol.5 , Issue.4 , pp. 537-550
    • Battiti, R.1
  • 5
    • 0036933407 scopus 로고    scopus 로고
    • Input feature selection by mutual information based on parzen window
    • Kwak, N., Choi, C.: Input Feature Selection by Mutual Information Based on Parzen Window. IEEE TPAMI 24(12), 1667-1671 (2002)
    • (2002) IEEE TPAMI , vol.24 , Issue.12 , pp. 1667-1671
    • Kwak, N.1    Choi, C.2
  • 7
    • 24344458137 scopus 로고    scopus 로고
    • Feature selection based on mutual information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy
    • DOI 10.1109/TPAMI.2005.159
    • 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, 1226-1238 (2005) (Pubitemid 41245053)
    • (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


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.