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Volumn 11, Issue 1, 2011, Pages

Predicting disease risks from highly imbalanced data using random forest

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; BIOLOGY; DISEASE PREDISPOSITION; ELECTRONIC MEDICAL RECORD; FACTUAL DATABASE; HUMAN; MEDICAL INFORMATION; METHODOLOGY; RISK ASSESSMENT; STATISTICS;

EID: 79960872876     PISSN: None     EISSN: 14726947     Source Type: Journal    
DOI: 10.1186/1472-6947-11-51     Document Type: Article
Times cited : (548)

References (24)
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    • Identifying persons with diabetes using Medicare claims data. Hebert P, et al. American Journal of Medical Quality 1999 14 6 270 10.1177/ 106286069901400607 10624032
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    • Hebert, P.1
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    • 10.1080/10410230802342176 18850390
    • Cancer coverage in general-audience and black newspapers. Cohen E, et al. Health Communication 2008 23 5 427 435 10.1080/10410230802342176 18850390
    • (2008) Health Communication , vol.23 , Issue.5 , pp. 427-435
    • Cohen, E.1
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    • 33845536164 scopus 로고    scopus 로고
    • The class imbalance problem: A systematic study
    • The class imbalance problem: A systematic study. Japkowicz N, Stephen S, Intelligent Data Analysis 2002 6 5 429 449
    • (2002) Intelligent Data Analysis , vol.6 , Issue.5 , pp. 429-449
    • Japkowicz, N.1    Stephen, S.2
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    • Random forests
    • DOI 10.1023/A:1010933404324
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    • Breiman, L.1
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    • A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data
    • A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data. Bjoern M, et al. BMC Bioinformatics 10
    • BMC Bioinformatics , vol.10
    • Bjoern, M.1
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    • An empirical comparison of selection measures for decision-tree induction
    • An empirical comparison of selection measures for decision-tree induction. Mingers J, Machine learning 1989 3 4 319 342
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    • Mingers, J.1
  • 22
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    • The use of the area under the ROC curve in the evaluation of machine learning algorithms
    • PII S0031320396001422
    • The use of the area under the ROC curve in the evaluation of machine learning algorithms. Bradley AP, Pattern Recognition 1997 30 1145 1159 10.1016/S0031-3203(96)00142-2 (Pubitemid 127406521)
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    • Bradley, A.P.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.