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J. Risk Financial Manag. 2016, 9(4), 13; doi:10.3390/jrfm9040013

Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function

School of Electrical & Automatic Engineering, Changshu Institute of Technology, Changshu 215500, China
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Academic Editor: Jingzhi Huang
Received: 28 June 2016 / Revised: 15 September 2016 / Accepted: 26 October 2016 / Published: 7 November 2016
(This article belongs to the Special Issue Credit Risk)
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Abstract

Due to the recent financial crisis and European debt crisis, credit risk evaluation has become an increasingly important issue for financial institutions. Reliable credit scoring models are crucial for commercial banks to evaluate the financial performance of clients and have been widely studied in the fields of statistics and machine learning. In this paper a novel fuzzy support vector machine (SVM) credit scoring model is proposed for credit risk analysis, in which fuzzy membership is adopted to indicate different contribution of each input point to the learning of SVM classification hyperplane. Considering the methodological consistency, support vector data description (SVDD) is introduced to construct the fuzzy membership function and to reduce the effect of outliers and noises. The SVDD-based fuzzy SVM model is tested against the traditional fuzzy SVM on two real-world datasets and the research results confirm the effectiveness of the presented method. View Full-Text
Keywords: fuzzy support vector machine; support vector data description; credit scoring fuzzy support vector machine; support vector data description; credit scoring
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Shi, J.; Xu, B. Credit Scoring by Fuzzy Support Vector Machines with a Novel Membership Function. J. Risk Financial Manag. 2016, 9, 13.

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J. Risk Financial Manag. EISSN 1911-8074 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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