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Volumn , Issue , 2004, Pages 286-295

A bayesian network framework for reject inference

Author keywords

Bayesian networks; Expectation maximization; Heckman estimator; Propensity scores; Reject inference; Sample selection bias

Indexed keywords

COMPUTATIONAL METHODS; DATA HANDLING; LEARNING ALGORITHMS; MATHEMATICAL MODELS; OPTIMIZATION; RANDOM PROCESSES;

EID: 12244265089     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1014052.1014085     Document Type: Conference Paper
Times cited : (22)

References (16)
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    • Cobb-Clark, D.A.1    Crossley, T.2
  • 4
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    • Does reject inference really improve the performance of application scoring models?
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    • (2002) Technical Report Working Paper Series No. 02/3 , vol.2 , Issue.3
    • Crook, J.1    Banasik, J.2
  • 5
    • 84867577175 scopus 로고    scopus 로고
    • The foundations of cost-sensitive learning
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    • Elkan, C.1
  • 6
    • 84860080955 scopus 로고    scopus 로고
    • An overview of model based reject inference for credit scoring
    • Utrecht University, Institute for Information and Computing Sciences
    • A. J. Feelders. An overview of model based reject inference for credit scoring. Technical report, Utrecht University, Institute for Information and Computing Sciences, http://www.cs.uu.nl/people/ad/mbrejinf.pdf.
    • Technical Report
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  • 8
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    • (1995) Technical Report , vol.MSR-TR-95-06
    • Heckerman, D.1
  • 9
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    • Sample selection bias as a specification error
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  • 13
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.