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Title: Integrating GA with Boosting Methods for Financial Distress Predictions
Authors: Liu, Hsin-Yu;Huang, Shian-Chang
Contributors: 企業管理學系
Keywords: Financial distress;Boosting algorithm;Genetic algorithm;AdaboostM1;Logitboost;Multiboost
Date: 2010-04
Issue Date: 2013-07-11T09:04:31Z
Publisher: 中華民國品質學會
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.
Relation: Journal of Quality, 17(2): 131-158
Appears in Collections:[Department of Business Administration] Periodical Articles

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