A Meta-learning Framework for Bankruptcy Prediction

Authors


Correspondence to: CHIH-FONG TSAI, Department of Information Management, National Central University, Jhongli City, Taiwan.

E-mail: cftsai@mgt.ncu.edu.tw

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. Copyright © 2011 John Wiley & Sons, Ltd.

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