한국지능정보시스템학회:학술대회논문집 (Proceedings of the Korea Inteligent Information System Society Conference)
- 한국지능정보시스템학회 2005년도 공동추계학술대회
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- Pages.509-515
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- 2005
Using Estimated Probability from Support Vector Machines for Credit Rating in IT Industry
- Hong, Tae-Ho (Devision of Business Administration,Pusan National University) ;
- Shin, Taek-Soo (Department of Management Information Systems, College of Government and Business, Yonsei University)
- 발행 : 2005.11.18
초록
Recently, support vector machines (SVMs) are being recognized as competitive tools as compared with other data mining techniques for solving pattern recognition or classification decision problems. Furthermore, many researches, in particular, have proved it more powerful than traditional artificial neural networks (ANNs)(Amendolia et al., 2003; Huang et al., 2004, Huang et al., 2005; Tay and Cao, 2001; Min and Lee, 2005; Shin et al, 2005; Kim, 2003). The classification decision, such as a binary or multi-class decision problem, used by any classifier, i.e. data mining techniques is cost-sensitive. Therefore, it is necessary to convert the output of the classifier into well-calibrated posterior probabilities. However, SVMs basically do not provide such probabilities. So it required to use any method to create probabilities (Platt, 1999; Drish, 2001). This study applies a method to estimate the probability of outputs of SVM to bankruptcy prediction and then suggests credit scoring methods using the estimated probability for bank's loan decision making.