Credit scoring with a data mining approach based on support vector machines

Baesens, 2003, Benchmarking state-of-the-art classification algorithms for credit scoring, Journal of the Operational Research Society, 54, 627, 10.1057/palgrave.jors.2601545

Brill, 1998, The importance of credit scoring models in improving cash flow and collection, Business Credit, 100, 16

Chang, C. C., & Lin, C. J. (2001) LIBSVM: a library for support vector machines. Available from http://www.csie.ntu.edu.tw/~cjlin/libsvm.

Chen, 2003, Credit scoring and rejected instances reassigning through evolutionary computation techniques, Expert Systems with Applications, 24, 433, 10.1016/S0957-4174(02)00191-4

Chen, 2004, The contribution of data mining to information science, Journal of Information Science, 30, 550, 10.1177/0165551504047928

Chen, Y.-W., & Lin, C.-J. (2005). Combining SVMs with various feature selection strategies. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/features.pdf.

Cristianini, 2000

Davis, 1992, Machine learning algorithms for credit-card applications, Journal of Mathematics Applied in Business and Industry, 4, 43

Desai, 1996, A comparison of neural networks and linear scoring models in the credit union environment, European Journal of Operational Research, 95, 24, 10.1016/0377-2217(95)00246-4

Fröhlich, H., & Chapelle, O. (2003). Feature selection for support vector machines by means of genetic algorithms. In Proceedings of the 15th IEEE international conference on tools with artificial intelligence, Sacramento, California, USA, pp. 142–148.

Garson, 1991, Interpreting neural-network connection weights, AI Expert, 6, 47

Goldberg, 1989

Guyon, 2002, Gene selection for cancer classification using support vector machines, Machine Learning, 46, 389, 10.1023/A:1012487302797

Henley, W. E. (1995). Statistical aspects of credit scoring. Dissertation, The Open University, Milton Keynes, UK.

Henley, 1996, A k-nearest neighbor classifier for assessing consumer credit risk, Statistician, 44, 77, 10.2307/2348414

Hoffmann, 2002, Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring, International Journal of Intelligent Systems, 17, 1067, 10.1002/int.10052

Holland, 1975

Hsieh, 2005, Hybrid mining approach in the design of credit scoring models, Expert Systems with Applications, 28, 655, 10.1016/j.eswa.2004.12.022

Hsu, C. W., Chang, C. C., & Lin, C. J. (2003). A practical guide to support vector classification. Available from http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf.

Hsu, 2002, A simple decomposition method for support vector machine, Machine Learning, 46, 219

Huang, 2004, Credit rating analysis with support vector machines and neural networks: a market comparative study, Decision Support Systems, 37, 543, 10.1016/S0167-9236(03)00086-1

Joachims, T. (1998). Text categorization with support vector machines. In Proceedings of European conference on machine learning (ECML), Chemintz, DE, pp.137–142.

John, G., Kohavi, R., & Peger, K. (1994). Irrelevant features and the subset selection problem. In Proceedings of the eleventh international conference on machine learning, San Mateo, CA, USA, pp. 121–129.

Kecman, 2001

Kohavi, 1997, Wrappers for feature subset selection, Artificial Intelligence, 97, 273, 10.1016/S0004-3702(97)00043-X

Koza, 1992

Lee, 2005, A two-stage hybrid credit scoring model using artificial neural networks and multivariate adaptive regression splines, Expert Systems with Applications, 28, 743, 10.1016/j.eswa.2004.12.031

Lee, 2002, Credit scoring using the hybrid neural discriminant technique, Expert Systems with Applications, 23, 245, 10.1016/S0957-4174(02)00044-1

Liu, 1998

Malhotra, 2002, Differentiating between good credits and bad credits using neuro-fuzzy systems, European Journal of Operational Research, 136, 190, 10.1016/S0377-2217(01)00052-2

Mao, 2004, Feature subset selection for support vector machines through discriminative function pruning analysis, IEEE Transactions on Systems, Man, and Cybernetics, 34, 60, 10.1109/TSMCB.2002.805808

Murphy, P. M., Aha, D. W. (2001). UCI repository of machine learning databases. Department of Information and Computer Science, University of California Irvine, CA. Available from http://www.ics.uci.edu/mlearn/MLRepository.html.

Ong, 2005, Building credit scoring models using genetic programming, Expert Systems with Applications, 29, 41, 10.1016/j.eswa.2005.01.003

Pontil, 1998, Support vector machines for 3D object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 637, 10.1109/34.683777

Quinlan, 1986, Induction of decision trees, Machine Learning, 1, 81, 10.1007/BF00116251

Quinlan, 1993

Reichert, 1983, An examination of the conceptual issues involved in developing credit-scoring models, Journal of Business and Economic Statistics, 1, 101, 10.2307/1391851

Salzberg, 1997, On comparing classifiers: pitfalls to avoid and a recommended approach, Data Mining and Knowledge Discovery, 1, 317, 10.1023/A:1009752403260

Scho˝lkopf, 2000

Somol, 2005, Filter-versus wrapper-based feature selection for credit scoring, International Journal of Intelligent Systems, 20, 985, 10.1002/int.20103

Tam, 1992, Managerial applications of the neural networks: the case of bank failure prediction, Management Science, 38, 926, 10.1287/mnsc.38.7.926

Thomas, 2000, A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers, International Journal of Forecasting, 16, 149, 10.1016/S0169-2070(00)00034-0

Vapnik, 1995

West, 2000, Neural network credit scoring models, Computers and Operations Research, 27, 1131, 10.1016/S0305-0548(99)00149-5

Weston, 2001, Feature selection for SVM, Vol. 13, 668

Yu, G. X., Ostrouchov, G., Geist, A., & Samatova, N. F. (2003). An SVM-based algorithm for identification of photosynthesis-specific genome features. In 2nd IEEE computer society bioinformatics conference, CA, USA, pp. 235–243.

Zhang, 2000, Neural networks for classification: a survey, IEEE Transactions on Systems, Man, and Cybernetics—Part C: Applications and Reviews, 30, 451, 10.1109/5326.897072