Bankruptcy prediction by supervised machine learning techniques: A comparative study

Chih Fong Tsai, Yu Hsin Lu, Yu Feng Hsu

研究成果: 書貢獻/報告類型篇章同行評審

2 引文 斯高帕斯(Scopus)


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.

主出版物標題Surveillance Technologies and Early Warning Systems
主出版物子標題Data Mining Applications for Risk Detection
發行者IGI Global
出版狀態已出版 - 2010


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