Çѱ¹Á¤º¸Åë½ÅÇÐȸ ³í¹®Áö (Journal of the Korea Institute of Information and Communication Engineering)
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ÇѱÛÁ¦¸ñ(Korean Title) |
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¿µ¹®Á¦¸ñ(English Title) |
An Empirical Analysis of Boosing of Neural Networks for Bankruptcy Prediction |
ÀúÀÚ(Author) |
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Myoung-Jong Kim
Dae-Ki Kang
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¿ø¹®¼ö·Ïó(Citation) |
VOL 14 NO. 01 PP. 0063 ~ 0069 (2010. 01) |
Çѱ۳»¿ë (Korean Abstract) |
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¿µ¹®³»¿ë (English 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.
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Å°¿öµå(Keyword) |
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Àΰø½Å°æ¸Á
±â¾÷ºÎ½Ç¿¹Ãø
Boosting
Neural Networks
Bankruptcy Prediction
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