Integrating GA with boosting methods for financial distress predictions

Hsinyu Liu, Shian-Chang Huang

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Financial distress is the most considerable and notable distress for companies. It also has a direct effect on its development and survival, and may result in a crisis in capital markets. Thus, financial distress prediction has been a critical issue in the area of academia and industry. The aims of this study are two folds: first, to compare prediction algorithms from data mining with traditional statistical methods, and second, to combine genetic algorithms (GAs) with boosting methods for developing a reliable and accurate model of bankruptcy prediction. The base classifiers we used are decision trees, logistic regressions, neural networks, and support vector machines. The boosting algorithms used are AdaboostM1, Logitboost, and Multiboost. The above algorithms are optimized by GA for input features. Empirical results indicated that integrating GA with AdaBoostM1 achieves the best performance.

Original languageEnglish
Pages (from-to)131-158
Number of pages28
JournalJournal of Quality
Volume17
Issue number2
Publication statusPublished - 2010 May 28

All Science Journal Classification (ASJC) codes

  • Safety, Risk, Reliability and Quality
  • Management Science and Operations Research

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