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Volumn , Issue , 2008, Pages 64-69

Investigation of random forest performance with cancer microarray data

Author keywords

[No Author keywords available]

Indexed keywords

CANCER CLASSIFICATION; CLASSIFICATION PERFORMANCE; CLASSIFICATION RATES; INHERENT CHARACTERISTICS; MACHINE LEARNING COMMUNITIES; MICROARRAY DATA; RANDOM FOREST CLASSIFIER; RESEARCH INTERESTS;

EID: 84883444193     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper
Times cited : (9)

References (14)
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    • Blair, E.1    Tibshirani, R.2
  • 2
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    • Random forest
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    • Breiman, L.1
  • 3
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    • Gene selection for cancer classification using support vector machines
    • I. Guyon, "Gene selection for cancer classification using support vector machines", Machine Learning, Vol. 46, 2002, pp 389-422.
    • (2002) Machine Learning , vol.46 , pp. 389-422
    • Guyon, I.1
  • 4
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    • Translating microarray data for diagnostic testing in childhood leukaemia
    • K. Hoffmann, "Translating microarray data for diagnostic testing in childhood leukaemia", BMC Cancer, 2006. 6:229.
    • (2006) BMC Cancer , vol.6 , pp. 229
    • Hoffmann, K.1
  • 5
    • 0034921979 scopus 로고    scopus 로고
    • Testing for differentially-expressed genes by maximum likelihood analysis of microarray data
    • T. Ideker, "Testing for differentially-expressed genes by maximum likelihood analysis of microarray data", Journal of Computational Biology, Vol. 7, 2000, pp 805-817.
    • (2000) Journal of Computational Biology , vol.7 , pp. 805-817
    • Ideker, T.1
  • 6
    • 0036450130 scopus 로고    scopus 로고
    • Gene selection by sequential wrapper approaches in microarray cancer class prediction
    • Inza, "Gene selection by sequential wrapper approaches in microarray cancer class prediction", Journal of Intelligent and Fuzzy Systems, 2002, pp. 24-34.
    • (2002) Journal of Intelligent and Fuzzy Systems , pp. 24-34
    • Inza1
  • 7
    • 0034954414 scopus 로고    scopus 로고
    • Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks
    • J. Khan, "Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks", Nature Medicine, Vol. 7, No. 6, 2001, pp 673-679.
    • (2001) Nature Medicine , vol.7 , Issue.6 , pp. 673-679
    • Khan, J.1
  • 9
    • 0035664710 scopus 로고    scopus 로고
    • Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method
    • L. Li, "Gene assessment and sample classification for gene expression data using a genetic algorithm/k-nearest neighbor method", Combinatorial Chemistry and High Throughput Screening. 2001, pp. 727-739.
    • (2001) Combinatorial Chemistry and High Throughput Screening , pp. 727-739
    • Li, L.1
  • 10
    • 0037076272 scopus 로고    scopus 로고
    • Dignosis of multiple cancer types by shrunken centroids of gene expression
    • R. Tibshirani, "Dignosis of multiple cancer types by shrunken centroids of gene expression", PNAS, Vol. 99, No. 10, 2002, pp. 6567-6572.
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  • 11
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    • Tumor classification by tissue microarray profiling: Random forest clustering applied to renal cell carcinoma
    • T. Shi, "Tumor classification by tissue microarray profiling: random forest clustering applied to renal cell carcinoma", Modern Pathology. Vol. 18, 2005, pp.547-557.
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  • 12
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    • Random forest: A classification and regression tool for compound classification and qsar modeling
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  • 14
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    • Probability estimates for multi-class classification by pair wise coupling
    • Ting-Fan Wu, Chih-Jen Lin and Ruby C. Weng, "probability estimates for multi-class classification by pair wise coupling", The journal of machine learning research, Volume 5, 2004.
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    • Wu, T.-F.1    Lin, C.-J.2    Weng, R.C.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.