Domain-Driven Classification Based on Multiple Criteria and Multiple Constraint-Level Programming for Intelligent Credit Scoring

作     者：He, Jing Zhang, Yanchun Shi, Yong Huang, Guangyan 

作者机构：Victoria Univ Sch Sci & Engn Melbourne Vic Australia Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China 

出 版 物：《IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING》 (IEEE Trans Knowl Data Eng)

年 卷 期：2010年第22卷第6期

页      面：826-838页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Australian Research Council (ARC) Linkage Project (Integration and Access Services for Smarter, Collaborative and Adaptive Whole of Water Cycle Management) ARC Discovery Project (Privacy Protection in Distributed Data Mining) National Natural Science Foundation of China [70621001, 70531040, 70840010] 973 Project of Chinese Ministry of Science and Technology [2004CB720103] BHP Billiton 

主　　题：Credit scoring domain-driven classification mathematical programming multiple criteria and multiple constraint-level programming fuzzy programming satisfying solution 

摘      要：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.