Abstract
Credit scoring is a very typical classification problem in Data Mining. Many classification methods have been presented in the literatures to tackle this problem. The decision tree method is a particularly effective method to build a classifier from the sample data. Decision tree classification method has higher prediction accuracy for the problems of classification, and can automatically generate classification rules. However, the original sample data sets used to generate the decision tree classification model often contain many noise or redundant data. These data will have a great impact on the prediction accuracy of the classifier. Therefore, it is necessary and very important to preprocess the original sample data. On this issue, a very effective approach is the rough sets. In rough sets theory, a basic problem that can be tackled using rough sets approach is reduction of redundant attributes. This paper presents a new credit scoring approach based on combination of rough sets theory and decision tree theory. The results of this study indicate that the process of reduction of attribute is very effective and our approach has good performance in terms of prediction accuracy.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hamiltion, A.G.: Logic for Mathematicians. Cambridge University Press, Cambridge (1988)
Huang, C.-L., Chen, M.-C., Wang, C.-J.: Credit scoring with a data mining approach based on support vector machines. Expert Systems with Applications (2006), doi: 10.1016/j.eswa 2006.07.007
Desai, V.S., Crook, J.N., Overstreet, G.A.: A comparison of neural networks and linear scoring models in the credit union environment. European Journal of Operational Research 95(1), 24–37 (1996)
Henley, W.E.: Statistical aspects of credit scoring. Dissertation, The Open University, Milton Keynes, UK (1995)
Henley, W.E., Hand, D.J.: A k-nearest neighbor classifier for assessing consumer credit risk. Statistician 44(1), 77–95 (1996)
Koza, J.R.: Genetic programming: On the programming of computers by means of natural selection. The MIT Press, Cambridge, MA (1992)
Kantardzic, M.: Data Mining: Concept, Models, Methods, and Algorithms. IEEE Press, America (2002)
Murphy, P.M., Aha, D.W.: UCI Repository of Machine Learning Database (2001), http://www.ics.uci.edu/~mlearn/MLRepository.html
Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11(5), 341–356 (1982)
Hu, Q., Zhao, H., Xie, Z., Yu, D.: Consistency Based Attribute Reduction. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 96–107. Springer, Heidelberg (2007)
Quinlan, J.R.: Introduction of decision trees. Machine Learning 1(1) (1986)
Quinlan, J.R.: C4.5: Programs for machine learning. Morgan Kaufmann, San Francisco (1993)
Skowron, A., Rauszer, C.: The discernibility matrices and function in information system. In: Slowinski, R. (ed.) Intelligent Decision support Handbook of Application and Advances of the Rough sets Theory, pp. 331–362. Kluwer Academic Publisher, Dordrecht (1991)
Yuan-Zhen, W., Xiao-Bing, P.: A Fast Algorithm for Reduction Based on Skowron Discernibility Matrix. Compute Science (in China) 32(4), 42–44 (2005)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhou, X., Zhang, D., Jiang, Y. (2008). A New Credit Scoring Method Based on Rough Sets and Decision Tree. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2008. Lecture Notes in Computer Science(), vol 5012. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68125-0_117
Download citation
DOI: https://doi.org/10.1007/978-3-540-68125-0_117
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68124-3
Online ISBN: 978-3-540-68125-0
eBook Packages: Computer ScienceComputer Science (R0)