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Volumn , Issue , 2008, Pages 17-24

Embedded gene selection for imbalanced microarray data analysis

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

EID: 63149099851     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IMSCCS.2008.33     Document Type: Conference Paper
Times cited : (9)

References (19)
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    • Small sample issue for microarraybased classification
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    • Comparison of discrimination methods for the classification of tumors using gene expression data
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    • Dudoit, S.1    Fridlyand, J.2    Speed, T.P.3
  • 6
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machines
    • I. Guyon, J. Weston, S. Barnhill, and V. Vapnik. Gene selection for cancer classification using support vector machines. Machine Learning, 46:389-422, 2002.
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    • Guyon, I.1    Weston, J.2    Barnhill, S.3    Vapnik, V.4
  • 10
    • 47249093354 scopus 로고    scopus 로고
    • Asymmetric bagging and feature selection for activities prediction of drug molecules
    • G.-Z. Li, H.-H. Meng, W.-C. Lu, J. Y. Yang, and M. Q. Yang. Asymmetric bagging and feature selection for activities prediction of drug molecules. BMC Bioinformatics, 9(Suppl 6):S7, 2008.
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    • Li, G.-Z.1    Meng, H.-H.2    Lu, W.-C.3    Yang, J.Y.4    Yang, M.Q.5
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    • Mining with rarity - problems and solutions: A unifying framework
    • G. M. Weiss. Mining with rarity - problems and solutions: A unifying framework. SIGKDD Explorations, 6(1):7-19, 2004.
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    • Weiss, G.M.1
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    • 33746677949 scopus 로고    scopus 로고
    • A stable gene selection in microarray data analysis
    • K. Yang, Z. Cai, J. Li, and G. Lin. A stable gene selection in microarray data analysis. BMC Bioinformatics, 7:228, 2006.
    • (2006) BMC Bioinformatics , vol.7 , pp. 228
    • Yang, K.1    Cai, Z.2    Li, J.3    Lin, G.4
  • 17
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    • Feature selection for text categorization on imbalanced data
    • Z. Zheng, X. Wu, and R. Srihari. Feature selection for text categorization on imbalanced data. SIGKDD Explorations, 6(1):80-89, 2004.
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    • MSVM-RFE: Extensions of SVM-RFE for multiclass gene selection on DNA microarray data
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  • 19
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    • Training cost-sensitive neural networks with methods addressing the class imbalance problem
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.