Least squares support vector machines ensemble models for credit scoring

Authors:
Ligang Zhou;Kin Keung Lai;Lean Yu
Affiliations:
Department of Management Sciences, City University of Hong Kong, Hong Kong;Department of Management Sciences, City University of Hong Kong, Hong Kong;Institute of Systems Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100080, China
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

Due to recent financial crisis and regulatory concerns of Basel II, credit risk assessment is becoming one of the most important topics in the field of financial risk management. Quantitative credit scoring models are widely used tools for credit risk assessment in financial institutions. Although single support vector machines (SVM) have been demonstrated with good performance in classification, a single classifier with a fixed group of training samples and parameters setting may have some kind of inductive bias. One effective way to reduce the bias is ensemble model. In this study, several ensemble models based on least squares support vector machines (LSSVM) are brought forward for credit scoring. The models are tested on two real world datasets and the results show that ensemble strategies can help to improve the performance in some degree and are effective for building credit scoring models.