Bankruptcy prediction using SVM models with a new approach to combine features selection and parameter optimisation

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

57 Scopus Citations
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  • Ligang Zhou
  • Kin Keung Lai
  • Jerome Yen

Related Research Unit(s)


Original languageEnglish
Pages (from-to)241-253
Journal / PublicationInternational Journal of Systems Science
Issue number3
Online published10 Sep 2012
Publication statusPublished - 2014


Due to the economic significance of bankruptcy prediction of companies for financial institutions, investors and governments, many quantitative methods have been used to develop effective prediction models. Support vector machine (SVM), a powerful classification method, has been used for this task; however, the performance of SVM is sensitive to model form, parameter setting and features selection. In this study, a new approach based on direct search and features ranking technology is proposed to optimise features selection and parameter setting for 1-norm and least-squares SVM models for bankruptcy prediction. This approach is also compared to the SVM models with parameter optimisation and features selection by the popular genetic algorithm technique. The experimental results on a data set with 2010 instances show that the proposed models are good alternatives for bankruptcy prediction. © 2014 Taylor and Francis.

Research Area(s)

  • bankruptcy prediction, direct search, genetic algorithm, support vector machines