Designing a Hybrid Intelligent Mining System for Credit Risk Evaluation
- 122 Downloads
In this study, a novel hybrid intelligent mining system integrating rough sets theory and support vector machines is developed to extract efficiently association rules from original information table for credit risk evaluation and analysis. In the proposed hybrid intelligent system, support vector machines are used as a tool to extract typical features and filter its noise, which are different from the previous studies where rough sets were only used as a preprocessor for support vector machines. Such an approach could reduce the information table and generate the final knowledge from the reduced information table by rough sets. Therefore, the proposed hybrid intelligent system overcomes the dificulty of extracting rules from a trained support vector machine classifier and possesses the robustness which is lacking for rough-set-based approaches. In addition, the effectiveness of the proposed hybrid intelligent system is illustrated with two real-world credit datasets.
Key wordsCredit risk evaluation hybrid intelligent system rough sets support vector machine
Unable to display preview. Download preview PDF.
- P. Makowski, Credit scoring branches out, Credit World, 1985, 75(1): 30–37.Google Scholar
- T. Van Gestel, B. Baesens, J. Garcia, and P. Van Dijcke, A support vector machine approach to credit scoring, Bank en Financiewezen, 2003, 2(1): 73–82.Google Scholar
- L. Yu, S. Y. Wang, K. K. Lai, and L. G. Zhou, Bio-Inspired Credit Risk Analysis–Computational Intelligence with Support Vector Machines, Springer-Verlag, Berlin, 2008.Google Scholar
- H. P. Nguyen, L. L. Phong, P. Santiprabhob, and B. De Baets, Approach to generation rules for expert systems using rough set theory, in Proceedings of the Joint 9th IFSA World Congress and 20th NAFIPS International Conference (ed. by M. H. Smith, W. A. Gruver, and William, H. L. O’Higgins), Vancouver, Canada, 2001, 877–882.Google Scholar
- J. Bazan, A. Skowron, and P. Synak, Dynamic reducts as a tool for extracting laws from decisions tables, in Proceedings of the 8th International Symposium on Methodologies for Intelligent Systems (ed. by G. Goos, J. Hartmanis, and J. van Leeuwen), Springer-Verlag, Berlin, 1994, 346–355.Google Scholar
- Y. Li and T. Fang, Study of forecasting algorithm for support vector machines based on rough sets, Journal of Data Acquisition & Processing, 2003, 18(2): 199–203.Google Scholar
- Z. Pawlak, Rough sets, in Rough Sets and Data Mining (ed. by T. Y. Lin and N. Cercone), Kluwer, Dordrecht, 1997, 3–8.Google Scholar
- R. R. Hashemi, F. R. Jelovsek, and M. Razzaghi, Developmental toxicity risk assessment: A rough set approach, International Journal of Methods of Information in Medicine, 1993, 32(1): 47–54.Google Scholar
- J. Brank, M. Grobelnik, N. Milic-Frayling, and D. Mladenic, Feature selection using support vector machines, in Proceeding of the 3th International Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields, Bologna, Italy, 2002, 25–27.Google Scholar
- R. Felix and T. Ushio, Rule induction from inconsistent and incomplete data using rough sets, in IEEE International Conference on Systems, Man, and Cybernetics, Tokyo, 1999, 5: 154–158.Google Scholar