A Meta-learning Framework for Bankruptcy Prediction
Article first published online: 9 NOV 2011
Copyright © 2011 John Wiley & Sons, Ltd.
Journal of Forecasting
Volume 32, Issue 2, pages 167–179, March 2013
How to Cite
Tsai, C.-F. and Hsu, Y.-F. (2013), A Meta-learning Framework for Bankruptcy Prediction. J. Forecast., 32: 167–179. doi: 10.1002/for.1264
- Issue published online: 28 JAN 2013
- Article first published online: 9 NOV 2011
- National Science Council of Taiwan. Grant Number: NSC 96-2416-H-194-010-MY3
- bankruptcy prediction;
- machine learning;
- stacked generalization
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. Copyright © 2011 John Wiley & Sons, Ltd.