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Credit Rating Analysis by the Decision-Tree Support Vector Machine with Ensemble Strategies

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Abstract

The recent financial tsunami and subprime crisis both triggered an excessive global financial decline. Since then, the ability to ensure the creditworthiness of firms, the development of a suitable credit rating mechanism and appropriate rating policies have become critical issues for most financial institutions. Credit rating is a multi-classification task. The decision-tree support vector machine (DTSVM) has been one of the most powerful models in dealing with multi-classification problems. However, the determination of important data features significantly affects the computation time and classification accuracy of DTSVM models. In addition, the inability to provide rules for decision-makers limits the practical applications of DTSVM models. This study proposes an M-DTSVM-RST model which integrates the unique strength of multiple feature selection strategies in feature determination, DTSVM in multi-classification, and rough set theory (RST) in rule generation. The advantages of the designed M-DTSVM-RST model include the ensemble learning ability in selecting essential attributes, the capability in yielding rules for decision-makers in multi-classification cases by the DTSVM technique with RST. Experimental results show that the M-DTSVM-RST model is a promising alternative in analyzing the credit rating problem.

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Acknowledgments

The authors would like to thank Ministry of Science and Technology of the Republic of China, Taiwan, for financially supporting this research under Contract Numbers NSC 101-2410-H-260-005-MY2 and MOST 103-2410-H-260-020.

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Authors and Affiliations

  1. Department of Information Management, National Chi Nan University, 1, University Rd., Puli, Nantou, 545, Taiwan, ROC

    Ping-Feng Pai & Yi-Shien Tan

  2. Department of International Business Studies, National Chi Nan University, 1, University Rd., Puli, Nantou, 545, Taiwan, ROC

    Ming-Fu Hsu

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  1. Ping-Feng Pai
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Correspondence to Ping-Feng Pai.

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Pai, PF., Tan, YS. & Hsu, MF. Credit Rating Analysis by the Decision-Tree Support Vector Machine with Ensemble Strategies. Int. J. Fuzzy Syst. 17, 521–530 (2015). https://doi.org/10.1007/s40815-015-0063-y

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