Improving experimental studies about ensembles of classifiers for bankruptcy prediction and credit scoring |
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| Affiliation: | 1. Nagoya Institute of Technology, Department of Computer Science, Gokisho, Showa, Nagoya, Aichi, 466-8555, Japan;2. University of the Ryukyus, Department of Electrical Engineering, Nakagami, Nishihara, Okinawa, 903-0213, Japan;1. Department of Computer Languages and Systems, University of Seville, Av Reina Mercedes S/N, 41012 Seville, Spain;2. School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, Middlesex UB7 7NU, United Kingdom;1. Department of Computer Science, Institute of Mathematics and Statistics, University of Sao Paulo, Rua do Matao, 1010, Cidade Universitaria, CEP 05508-090 Sao Paulo, SP, Brazil;2. Computing Institute, Federal University of Alagoas, Campus A.C. Simoes, BR 104, Norte, km 97, Cidade Universitaria, CEP 57072-970 Maceio, AL, Brazil;3. Department of Computer Systems, Institute of Mathematics and Computional Sciences, University of Sao Paulo, Avenida Trabalhador Sao-carlense, 400 Centro, CEP 13566-590 Sao Carlos, SP, Brazil;1. Center of Informatics, CIn, Federal University of Pernambuco, UFPE, Hélio Ramos Av., 50740560 Recife, Brazil;2. Philips Research, 345 Scarborough Road, Briarcliff Manor, NY 10510, USA;3. Intel Corporation, 2111 NE 25th Avenue, Hillsboro, OR 97124, USA |
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| Abstract: | Previous studies about ensembles of classifiers for bankruptcy prediction and credit scoring have been presented. In these studies, different ensemble schemes for complex classifiers were applied, and the best results were obtained using the Random Subspace method. The Bagging scheme was one of the ensemble methods used in the comparison. However, it was not correctly used. It is very important to use this ensemble scheme on weak and unstable classifiers for producing diversity in the combination. In order to improve the comparison, Bagging scheme on several decision trees models is applied to bankruptcy prediction and credit scoring. Decision trees encourage diversity for the combination of classifiers. Finally, an experimental study shows that Bagging scheme on decision trees present the best results for bankruptcy prediction and credit scoring. |
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| Keywords: | Bankruptcy prediction Credit scoring Ensembles of classifiers Decision trees Imprecise Dirichlet model |
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