Feature selection to diagnose a business crisis by using a real GA-based support vector machine: An empirical study

Liang-Hsuan Chen, Huey Der Hsiao

Research output: Contribution to journalArticle

52 Citations (Scopus)


This research is aimed at establishing the diagnosis models for business crises through integrating a real-valued genetic algorithm to determine the optimum parameters and SVM to perform learning and classification on data. After finishing the training processes, the proposed GA-SVM can reach a prediction accuracy of up to 95.56% for all the tested business data. Particularly, only six influential features are included in the proposed model with intellectual capital and financial features after the 2-phase selecting process; the six features are ordinary and widely available from public business reports. The proposed GA-SVM is available for business managers to conduct self-diagnosis in order to realize whether business units are really facing a crisis.

Original languageEnglish
Pages (from-to)1145-1155
Number of pages11
JournalExpert Systems With Applications
Issue number3
Publication statusPublished - 2008 Oct 1


All Science Journal Classification (ASJC) codes

  • Engineering(all)
  • Computer Science Applications
  • Artificial Intelligence

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