A meta-learning framework for bankruptcy prediction

Chih Fong Tsai, Yu Feng Hsu

Research output: Contribution to journalArticlepeer-review

23 Scopus citations

Abstract

The implication of corporate bankruptcy prediction is important to financial institutions when making lending decisions. In related studies, many bankruptcy prediction models have been developed based on some machine-learning techniques. This paper presents a meta-learning framework, which is composed of two-level classifiers for bankruptcy prediction. The first-level multiple classifiers perform the data reduction task by filtering out unrepresentative training data. Then, the outputs of the first-level classifiers are utilized to create the second-level single (meta) classifier. The experiments are based on five related datasets and the results show that the proposed meta-learning framework provides higher prediction accuracy rates and lower type I/II errors when compared with the stacked generalization classifier and other three widely developed baselines, such as neural networks, decision trees, and logistic regression.

Original languageEnglish
Pages (from-to)167-179
Number of pages13
JournalJournal of Forecasting
Volume32
Issue number2
DOIs
StatePublished - Mar 2013

Keywords

  • bankruptcy prediction
  • machine learning
  • meta-learning
  • stacked generalization

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