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Volumn , Issue , 2010, Pages

A random feature selection approach for neural network ensembles: Considering diversity

Author keywords

Diversity measurement; Ensemble feature selection; Ensemble learning; GASEN

Indexed keywords

COMPONENT NETWORKS; DIVERSITY MEASUREMENT; ENSEMBLE FEATURE SELECTION; ENSEMBLE FEATURE SELECTIONS; ENSEMBLE LEARNING; FEATURE SELECTION; FEATURE SELECTION METHODS; FEATURE SUBSPACE; GASEN; GENERALIZATION ABILITY; NEURAL NETWORK ENSEMBLES; NEW MODEL; TRAINING PROCESS; WHOLE PROCESS;

EID: 79951612795     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CISE.2010.5677051     Document Type: Conference Paper
Times cited : (3)

References (6)
  • 1
    • 78649934709 scopus 로고    scopus 로고
    • Irvine, CA: School of Information and Computer Science
    • Frank, A. & Asuncion, A. (2010). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.
    • (2010) UCI Machine Learning Repository
    • Frank, A.1    Asuncion, A.2
  • 2
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • L. Breiman. Random Forests. Machine Learning, 45, 5-32,2001.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 6
    • 0036567392 scopus 로고    scopus 로고
    • Ensembling neural networks: Many could be better than all
    • DOI 10.1016/S0004-3702(02)00190-X, PII S000437020200190X
    • Z. H. Zhou, J. Wu, and W. Tang. "Ensembling neural networks: Many could be better than all," Artificial Intelligence, 137(1-2): 239-263, 2002. (Pubitemid 34405220)
    • (2002) Artificial Intelligence , vol.137 , Issue.1-2 , pp. 239-263
    • Zhou, Z.-H.1    Wu, J.2    Tang, W.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.