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http://dx.doi.org/10.6109/jkiice.2010.14.1.063

An Empirical Analysis of Boosing of Neural Networks for Bankruptcy Prediction  

Kim, Myoung-Jong (동서대학교 경영학부)
Kang, Dae-Ki (동서대학교 컴퓨터정보공학부)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.14, no.1, 2010 , pp. 63-69 More about this Journal
Abstract
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. This paper performs an empirical comparison of Boosted neural networks and traditional neural networks on bankruptcy prediction tasks. Experimental results on Korean firms indicated that the boosted neural networks showed the improved performance over traditional neural networks.
Keywords
Boosting; Neural Networks; Bankruptcy Prediction;
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