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Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study

Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study

Chih-Fong Tsai, Yu-Hsin Lu, Yu-Feng Hsu
Copyright: © 2012 |Pages: 16
ISBN13: 9781609608187|ISBN10: 1609608186|EISBN13: 9781609608194
DOI: 10.4018/978-1-60960-818-7.ch319
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MLA

Tsai, Chih-Fong, et al. "Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study." Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, IGI Global, 2012, pp. 668-683. https://doi.org/10.4018/978-1-60960-818-7.ch319

APA

Tsai, C., Lu, Y., & Hsu, Y. (2012). Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study. In I. Management Association (Ed.), Machine Learning: Concepts, Methodologies, Tools and Applications (pp. 668-683). IGI Global. https://doi.org/10.4018/978-1-60960-818-7.ch319

Chicago

Tsai, Chih-Fong, Yu-Hsin Lu, and Yu-Feng Hsu. "Bankruptcy Prediction by Supervised Machine Learning Techniques: A Comparative Study." In Machine Learning: Concepts, Methodologies, Tools and Applications, edited by Information Resources Management Association, 668-683. Hershey, PA: IGI Global, 2012. https://doi.org/10.4018/978-1-60960-818-7.ch319

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Abstract

It is very important for financial institutions which are capable of accurately predicting business failure. In literature, numbers of bankruptcy prediction models have been developed based on statistical and machine learning techniques. In particular, many machine learning techniques, such as neural networks, decision trees, etc. have shown better prediction performances than statistical ones. However, advanced machine learning techniques, such as classifier ensembles and stacked generalization have not been fully examined and compared in terms of their bankruptcy prediction performances. The aim of this chapter is to compare two different machine learning techniques, one statistical approach, two types of classifier ensembles, and three stacked generalization classifiers over three related datasets. The experimental results show that classifier ensembles by weighted voting perform the best in term of predication accuracy. On the other hand, for Type II errors on average stacked generalization and single classifiers perform better than classifier ensembles.

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