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Volumn 3992 LNCS - II, Issue , 2006, Pages 670-677

Boost feature subset selection: A new gene selection algorithm for microarray dataset

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ARRAYS; GENES; PROBLEM SOLVING; SET THEORY;

EID: 33746645146     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11758525_91     Document Type: Conference Paper
Times cited : (15)

References (15)
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  • 3
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    • New feature subset selection procedures for classification of expression profiles
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    • (2002) Genome Biology , vol.3 , Issue.4
    • Bø, T.H.1    Jonassen, I.2
  • 5
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    • Experiments with a new boosting algorithm
    • Yoav Freund and Robert E. Schapire. Experiments with a new boosting algorithm. In in Proc. ICML 1996, 1996.
    • (1996) Proc. ICML 1996
    • Freund, Y.1    Schapire, R.E.2
  • 6
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    • Molecular classifications of cancer: Class discovery and class prediction by gene expression monitoring
    • T. R. Golub et al. Molecular classifications of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286(5439):531-7, 1999.
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    • Golub, T.R.1
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    • 0037976550 scopus 로고    scopus 로고
    • Improved gene selection for classification of microarrays
    • J. Jaeger, R. Sengupta, and W. L. Ruzzo. Improved gene selection for classification of microarrays. In Proc. PSB, 2003.
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    • Jaeger, J.1    Sengupta, R.2    Ruzzo, W.L.3
  • 11
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    • Hykgene: A hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data
    • Yuhang Wang, Fillia S. Makedon, James C. Ford, and Justin Pearlman. Hykgene: a hybrid approach for selecting marker genes for phenotype classification using microarray gene expression data. Bioinformatics, 21(8):1530-1537, 2005.
    • (2005) Bioinformatics , vol.21 , Issue.8 , pp. 1530-1537
    • Wang, Y.1    Makedon, F.S.2    Ford, J.C.3    Pearlman, J.4
  • 13
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    • Feature selection for high-dimensional genomic microarray data
    • Morgan Kaufmann, San Francisco, CA
    • E. P. Xing, M. I. Jordan, and R. M. Karp. Feature selection for high-dimensional genomic microarray data. In Proc. 18th International Conf. on Machine Learning, pages 601-608. Morgan Kaufmann, San Francisco, CA, 2001.
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    • Virtual gene: Using correlations between genes to select informative genes on microarray datasets
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    • Redundancy based feature selection for microarray data
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.