Support vector machine based multiagent ensemble learning for credit risk evaluation

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

98 Scopus Citations
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Author(s)

  • Lean Yu
  • Wuyi Yue
  • Shouyang Wang
  • K. K. Lai

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)1351-1360
Journal / PublicationExpert Systems with Applications
Volume37
Issue number2
Publication statusPublished - Mar 2010

Abstract

In this paper, a four-stage support vector machine (SVM) based multiagent ensemble learning approach is proposed for credit risk evaluation. In the first stage, the initial dataset is divided into two independent subsets: training subset (in-sample data) and testing subset (out-of-sample data) for training and verification purposes. In the second stage, different SVM learning paradigms with much dissimilarity are constructed as intelligent agents for credit risk evaluation. In the third stage, multiple individual SVM agents are trained using training subsets and the corresponding evaluation results are also obtained. In the final stage, all individual results produced by multiple SVM agents in the previous stage are aggregated into an ensemble result. In particular, the impact of the diversity of individual intelligent agents on the generalization performance of the SVM-based multiagent ensemble learning system is examined and analyzed. For illustration, one corporate credit card application approval dataset is used to verify the effectiveness of the SVM-based multiagent ensemble learning system. © 2009 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Credit risk evaluation, Diversity strategy, Ensemble strategy, Multiagent ensemble learning, Support vector machine (SVM)

Citation Format(s)

Support vector machine based multiagent ensemble learning for credit risk evaluation. / Yu, Lean; Yue, Wuyi; Wang, Shouyang; Lai, K. K.

In: Expert Systems with Applications, Vol. 37, No. 2, 03.2010, p. 1351-1360.

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