Comparison of the Hybrid Credit Scoring Models Based on Various Classifiers

Fei-Long Chen and Feng-Chia Li
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Fei-Long Chen: National Tsing Hua University, Taiwan
Feng-Chia Li: Jen-Teh Junior College and National Tsing Hua University, Taiwan

International Journal of Intelligent Information Technologies (IJIIT), 2010, vol. 6, issue 3, 56-74

Abstract: Credit scoring is an important topic for businesses and socio-economic establishments collecting huge amounts of data, with the intention of making the wrong decision obsolete. In this paper, the authors propose four approaches that combine four well-known classifiers, such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back-Propagation Network (BPN) and Extreme Learning Machine (ELM). These classifiers are used to find a suitable hybrid classifier combination featuring selection that retains sufficient information for classification purposes. In this regard, different credit scoring combinations are constructed by selecting features with four approaches and classifiers than would otherwise be chosen. Two credit data sets from the University of California, Irvine (UCI), are chosen to evaluate the accuracy of the various hybrid features selection models. In this paper, the procedures that are part of the proposed approaches are described and then evaluated for their performances.

Date: 2010
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