Abstract
Financial distress is the most considerable and notable distress for companies. It also has a direct effect on its development and survival, and may result in a crisis in capital markets. Thus, financial distress prediction has been a critical issue in the area of academia and industry. The aims of this study are two folds: first, to compare prediction algorithms from data mining with traditional statistical methods, and second, to combine genetic algorithms (GAs) with boosting methods for developing a reliable and accurate model of bankruptcy prediction. The base classifiers we used are decision trees, logistic regressions, neural networks, and support vector machines. The boosting algorithms used are AdaboostM1, Logitboost, and Multiboost. The above algorithms are optimized by GA for input features. Empirical results indicated that integrating GA with AdaBoostM1 achieves the best performance.
| Original language | English |
|---|---|
| Pages (from-to) | 131-158 |
| Number of pages | 28 |
| Journal | Journal of Quality |
| Volume | 17 |
| Issue number | 2 |
| Publication status | Published - 2010 May 28 |
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All Science Journal Classification (ASJC) codes
- Safety, Risk, Reliability and Quality
- Management Science and Operations Research
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Integrating GA with boosting methods for financial distress predictions. / Liu, Hsinyu; Huang, Shian-Chang.
In: Journal of Quality, Vol. 17, No. 2, 28.05.2010, p. 131-158.Research output: Contribution to journal › Article
TY - JOUR
T1 - Integrating GA with boosting methods for financial distress predictions
AU - Liu, Hsinyu
AU - Huang, Shian-Chang
PY - 2010/5/28
Y1 - 2010/5/28
N2 - Financial distress is the most considerable and notable distress for companies. It also has a direct effect on its development and survival, and may result in a crisis in capital markets. Thus, financial distress prediction has been a critical issue in the area of academia and industry. The aims of this study are two folds: first, to compare prediction algorithms from data mining with traditional statistical methods, and second, to combine genetic algorithms (GAs) with boosting methods for developing a reliable and accurate model of bankruptcy prediction. The base classifiers we used are decision trees, logistic regressions, neural networks, and support vector machines. The boosting algorithms used are AdaboostM1, Logitboost, and Multiboost. The above algorithms are optimized by GA for input features. Empirical results indicated that integrating GA with AdaBoostM1 achieves the best performance.
AB - Financial distress is the most considerable and notable distress for companies. It also has a direct effect on its development and survival, and may result in a crisis in capital markets. Thus, financial distress prediction has been a critical issue in the area of academia and industry. The aims of this study are two folds: first, to compare prediction algorithms from data mining with traditional statistical methods, and second, to combine genetic algorithms (GAs) with boosting methods for developing a reliable and accurate model of bankruptcy prediction. The base classifiers we used are decision trees, logistic regressions, neural networks, and support vector machines. The boosting algorithms used are AdaboostM1, Logitboost, and Multiboost. The above algorithms are optimized by GA for input features. Empirical results indicated that integrating GA with AdaBoostM1 achieves the best performance.
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UR - http://www.scopus.com/inward/citedby.url?scp=77952646285&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:77952646285
VL - 17
SP - 131
EP - 158
JO - Journal of Quality
JF - Journal of Quality
SN - 1022-0690
IS - 2
ER -