期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 36, 期 2, 页码 2625-2632出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.01.024
关键词
Credit scoring; Link analysis ranking algorithm; Support vector machine
类别
资金
- National Natural Science Foundation of China [60433020, 60673099]
- National Education Ministry of China
Credit scoring is very important in business, especially in banks. We want to describe a person who is a good credit or a bad one by evaluating his/her credit. We systematically proposed three link analysis algorithms based oil the preprocess of support vector machine, to estimate all applicant's credit so as to decide whether a bank should provide a loan to the applicant. The proposed algorithms have two major phases which are called input weighted adjustor and class by support vector machine-based models. In the first phase, we consider the link relation by link analysis and integrate the relation of applicants through their information into input vector of next phase. In the other phase, an algorithm is proposed based on general support vector machine model. A real world credit dataset is used to evaluate the performance of the proposed algorithms by 10-fold cross-validation method. It is shown that the genetic link analysis ranking methods have higher performance in terms of classification accuracy. (C) 2008 Elsevier Ltd. All rights reserved.
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