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Volumn 7413 LNAI, Issue , 2012, Pages 180-189

On the homogeneous ensembling with balanced random sets and boosting

Author keywords

Blending; Boosting; Classification; Cross validation; Decision trees; Ensembling; Neural nets

Indexed keywords

BLENDING; DATA MINING; DECISION TREES; NEURAL NETWORKS; ROUGH SET THEORY;

EID: 84892835532     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-32115-3_21     Document Type: Conference Paper
Times cited : (1)

References (9)
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    • Wang, W.: Some fundamental issues in ensemble methods. In: World Congress on Computational Intelligence, Hong Kong, pp. 2244–2251. IEEE (2008)
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    • Wang, W.1
  • 3
    • 47349102423 scopus 로고    scopus 로고
    • Classification of imbalanced data with random sets and mean-variance filtering
    • Nikulin, V.: Classification of imbalanced data with random sets and mean-variance filtering. International Journal of Data Warehousing and Mining 4(2), 63–78 (2008)
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  • 5
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    • A decision-theoretic generalization of on-line learning and an application to boosting
    • Freund, Y., Schapire, R.: A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences 55, 119–139 (1997)
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    • Freund, Y.1    Schapire, R.2
  • 6
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    • Sample selection bias as a specification error
    • Heckerman, J.: Sample selection bias as a specification error. Econometrica 47(1), 153–161 (1979)
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    • Heckerman, J.1
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    • The best analyst win
    • Carpenter, J.: the best analyst win. Science 331, 698–699 (2011)
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    • Carpenter, J.1
  • 9
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    • Cross-validation of covariance structures
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    • Cudeck, R.1    Browne, M.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.