Credit risk evaluation with kernel-based affine subspace nearest points learning method

Authors:
Xiaofei Zhou;Wenhan Jiang;Yong Shi;Yingjie Tian
Affiliations:
Graduate University of Chinese Academy of Sciences, Beijing 100190, China;First Research Institute of Ministry of Public Security, Beijing 100048, China and Tsinghua University, Department of Electronic Engineering, Beijing 100084, China;Graduate University of Chinese Academy of Sciences, Beijing 100190, China and College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE 68182, USA;Graduate University of Chinese Academy of Sciences, Beijing 100190, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

Credit risk evaluation has long been an important and widely studied topic in bank lending decisions and profitability. Currently emerging data mining and machine learning techniques, such as support vector machine (SVM), have been discussed widely in credit risk evaluation. In this paper a new kernel-based learning method called kernel affine subspace nearest point (KASNP) approach is proposed for credit risk evaluation. KASNP approach is derived from the nearest point problem of SVM, which extends the areas searched for the nearest points from the convex hulls in SVM to affine subspaces. Similar to SVM, KASNP can also classify the typical nonlinear two-spiral problem well. But unlike SVM to solve the difficult convex quadratic programming problem, KASNP is an unconstrained optimal problem whose solution can be directly computed. We apply KASNP for credit evaluation, and the experiments on three credit datasets show that the proposed KASNP is more competitive for creditors classification.