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Volumn WS-05-09, Issue , 2005, Pages 46-51

Evolutionary ensembles: Combining learning agents using genetic algorithms

Author keywords

[No Author keywords available]

Indexed keywords

COLLABORATION; LEARNING AGENTS; MULTI-AGENT FRAMEWORK; WEIGHTED VOTING;

EID: 33646056708     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (18)

References (24)
  • 2
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    • Bradshaw, J. 1997. Software Agents. Cambridge, MA: The MIT Press.
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    • Bradshaw, J.1
  • 3
    • 0030211964 scopus 로고    scopus 로고
    • Bagging predictors
    • Breiman, L. 1996. Bagging predictors. Machine Learning 24(2):123-140.
    • (1996) Machine Learning , vol.24 , Issue.2 , pp. 123-140
    • Breiman, L.1
  • 4
    • 0032634129 scopus 로고    scopus 로고
    • Pasting bites together for prediction in large data sets
    • Breiman, L. 1999. Pasting bites together for prediction in large data sets. Machine Learning 36(1,2):85-103.
    • (1999) Machine Learning , vol.36 , Issue.1-2 , pp. 85-103
    • Breiman, L.1
  • 5
    • 33745786017 scopus 로고    scopus 로고
    • A multistrategy approach for digital text categorization from unbalanced documents
    • Castillo, M. D., and Serrano, J. 2004. A multistrategy approach for digital text categorization from unbalanced documents. ACM SIGKDD Explorations Newsletter 6:70-79.
    • (2004) ACM SIGKDD Explorations Newsletter , vol.6 , pp. 70-79
    • Castillo, M.D.1    Serrano, J.2
  • 7
    • 0034250160 scopus 로고    scopus 로고
    • An empirical comparison of three methods for constructing ensembles of decision trees: Bagging, boosting and randomization
    • Dietterich, T. 2000a. An empirical comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization. Machine Learning 40(2):139-157.
    • (2000) Machine Learning , vol.40 , Issue.2 , pp. 139-157
    • Dietterich, T.1
  • 14
    • 0037403516 scopus 로고    scopus 로고
    • Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
    • Kuncheva, L., and Whitaker, C. 2003. Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy. Machine Learning 51:181-207.
    • (2003) Machine Learning , vol.51 , pp. 181-207
    • Kuncheva, L.1    Whitaker, C.2
  • 15
    • 24144490154 scopus 로고    scopus 로고
    • Diversity in multiple classifier systems
    • Kuncheva, L. 2005. Diversity in multiple classifier systems. Information Fusion 6:2-3.
    • (2005) Information Fusion , vol.6 , pp. 2-3
    • Kuncheva, L.1
  • 16
    • 0013371845 scopus 로고    scopus 로고
    • Evolving heterogeneous neural agents by local selection
    • Honavar, V.; Patel, M.; and Balakrishnan, K., eds., Cambridge, MA: MIT Press
    • Menczer, F.; Street, W. N.; and Degeratu, M. 2000. Evolving heterogeneous neural agents by local selection. In Honavar, V.; Patel, M.; and Balakrishnan, K., eds., Advances in the Evolutionary Synthesis of Neural Systems. Cambridge, MA: MIT Press.
    • (2000) Advances in the Evolutionary Synthesis of Neural Systems
    • Menczer, F.1    Street, W.N.2    Degeratu, M.3
  • 17
    • 0032596573 scopus 로고    scopus 로고
    • Feature selection for ensembles
    • Opitz, D. 1999. Feature selection for ensembles. In AAAI/IAAI, 379-384.
    • (1999) AAAI/IAAI , pp. 379-384
    • Opitz, D.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.