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자료유형
학술대회자료
저자정보
저널정보
한국지능정보시스템학회 한국지능정보시스템학회 학술대회논문집 한국지능정보시스템학회 2004년 춘계학술대회논문집
발행연도
2004.6
수록면
111 - 120 (10page)

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Corporate financial distress and bankruptcy prediction is one of the major application areas of artificial neural networks (ANNs) in finance and management. ANNs have showed high prediction performance in this area, but sometimes are confronted with inconsistent and unpredictable performance for noisy data. In addition. it may not be possible to train ANN or the training task cannot be effectively carried out wi/hout data reduction when the amount of data is so large because training the large data set needs much processing time and additional costs of collecting data. Instance selection is one of popular methods for dimensionality reduction and is directly related to data reduction. Although some researchers have addressed the needfor instance selection in instance-based learning algorithms, there is little research on instance selection for ANN. This study proposes a genetic algorithm (GA) approach to instance selection in ANN for bankruptcy prediction. In this study. we use ANN supported by a GA to optimize he connection weights between layers and select relevant instances. It is expected that the globally evolved weights mitigate the well-known limitations of gradient descent algorithm of backpropagation algorithm. In addition, genetically selected instances will shorten the learning time and enhance prediction performance. This study will compare the proposed model with other major data mining techniques. Experimental results show that the GA approach is a promising method for instance selection in ANN.

목차

Abstract

1.Introduction

2.Research background

3.A GA approach to instance selection for ANN

4.Comparative analysis on corporate bankruptcy prediction

5.Concluding remarks

References

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UCI(KEPA) : I410-ECN-0101-2009-003-014302229