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http://dx.doi.org/10.9716/KITS.2015.14.4.121

Optimization of Random Subspace Ensemble for Bankruptcy Prediction  

Min, Sung-Hwan (한림대학교 경영학부)
Publication Information
Journal of Information Technology Services / v.14, no.4, 2015 , pp. 121-135 More about this Journal
Abstract
Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.
Keywords
Random Subspace; Ensemble; Bankruptcy Prediction; Genetic Algorithms;
Citations & Related Records
Times Cited By KSCI : 6  (Citation Analysis)
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