Least squares support vector machines ensemble models for credit scoring

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

102 Scopus Citations
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Author(s)

  • Ligang Zhou
  • Kin Keung Lai
  • Lean Yu

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)127-133
Journal / PublicationExpert Systems with Applications
Volume37
Issue number1
Publication statusPublished - Jan 2010

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. © 2009 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Credit scoring, Ensemble model, Support vector machines

Citation Format(s)

Least squares support vector machines ensemble models for credit scoring. / Zhou, Ligang; Lai, Kin Keung; Yu, Lean.

In: Expert Systems with Applications, Vol. 37, No. 1, 01.2010, p. 127-133.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal