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Volumn , Issue , 2004, Pages 258-263

Aggregation ordering in bagging

Author keywords

Bagging; Decision trees; Ensemble pruning; Machine learning

Indexed keywords

COMPUTATIONAL COMPLEXITY; DATA ACQUISITION; DATABASE SYSTEMS; GENETIC ALGORITHMS; HEURISTIC METHODS; LEARNING SYSTEMS; TREES (MATHEMATICS); VECTORS;

EID: 11144281845     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (143)

References (19)
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    • (1998) The Annals of Statistics , vol.12 , Issue.5 , pp. 1651-1686
    • Schapire, R.1    Freund, Y.2    Bartlett, P.3    Lee, W.4
  • 6
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    • E. Bauer and R. Kohavi, An empirical comparison of voting classification algorithms: Bagging, boosting, and variants, Machine Learning, 36(1-2), 1999, 105-139
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 105-139
    • Bauer, E.1    Kohavi, R.2
  • 8
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    • An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
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  • 13
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    • Ensembling neural networks: Many could be better than all
    • Z.H. Zhou, J. Wu and W. Tang, Ensembling neural networks: Many could be better than all, Artificial Intelligence, 137(1-2), 2002, 239-263
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    • Zhou, Z.H.1    Wu, J.2    Tang, W.3
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  • 17
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    • Bias, variance, and arcing classifiers
    • Statistics Department, University of California
    • L. Breiman, Bias, variance, and arcing classifiers, Technical Report 460, Statistics Department, University of California, 1996
    • (1996) Technical Report , vol.460
    • Breiman, L.1
  • 18
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    • Random forests
    • L. Breiman, Random forests, Machine Learning, 45(1), 2001, 5-32
    • (2001) Machine Learning , vol.45 , Issue.1 , pp. 5-32
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.