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Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring

He, Jing, Zhang, Yanchun, Shi, Yong and Huang, Guangyan 2010, Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring, IEEE transactions on knowledge and data engineering, vol. 22, no. 6, pp. 826-838, doi: 10.1109/TKDE.2010.43.

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Title Domain-driven classification based on multiple criteria and multiple constraint-level programming for intelligent credit scoring
Author(s) He, Jing
Zhang, Yanchun
Shi, Yong
Huang, Guangyan
Journal name IEEE transactions on knowledge and data engineering
Volume number 22
Issue number 6
Start page 826
End page 838
Total pages 13
Publisher Springer
Place of publication Berlin, Germany
Publication date 2010-06
ISSN 1041-4347
Summary Extracting knowledge from the transaction records and the personal data of credit card holders has great profit potential for the banking industry. The challenge is to detect/predict bankrupts and to keep and recruit the profitable customers. However, grouping and targeting credit card customers by traditional data-driven mining often does not directly meet the needs of the banking industry, because data-driven mining automatically generates classification outputs that are imprecise, meaningless, and beyond users' control. In this paper, we provide a novel domain-driven classification method that takes advantage of multiple criteria and multiple constraint-level programming for intelligent credit scoring. The method involves credit scoring to produce a set of customers' scores that allows the classification results actionable and controllable by human interaction during the scoring process. Domain knowledge and experts' experience parameters are built into the criteria and constraint functions of mathematical programming and the human and machine conversation is employed to generate an efficient and precise solution. Experiments based on various data sets validated the effectiveness and efficiency of the proposed methods. © 2006 IEEE.
Language eng
DOI 10.1109/TKDE.2010.43
Field of Research 08 Information And Computing Sciences
080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category C1.1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2010, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30083658

Document type: Journal Article
Collection: School of Information Technology
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