Ensemble with neural networks for bankruptcy prediction

Authors:
Myoung-Jong Kim;Dae-Ki Kang
Affiliations:
Department of Business Administration, Dongseo University, San69-1, Churye-2Dong, Sasang-Gu, Busan 617-716, Republic of Korea;Department of Computer and Information Engineering, Dongseo University, San69-1, Churye-2Dong, Sasang-Gu, Busan 617-716, Republic of Korea
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

In a bankruptcy prediction model, the accuracy is one of crucial performance measures due to its significant economic impact. Ensemble is one of widely used methods for improving the performance of classification and prediction models. Two popular ensemble methods, Bagging and Boosting, have been applied with great success to various machine learning problems using mostly decision trees as base classifiers. In this paper, we propose an ensemble with neural network for improving the performance of traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the bagged and the boosted neural networks showed the improved performance over traditional neural networks.