Support vector machines for credit scoring and discovery of significant features

Baesens, 2003, Benchmarking state-of-the-art classification algorithms for credit scoring, Journal of the Operational Research Society, 54, 1082, 10.1057/palgrave.jors.2601545 Cristianini, 2000 Duda, 2001 Gayler, 2006, Comment: Classifier technology and the illusion of progress – Credit scoring, Statistical Science, 21, 19, 10.1214/088342306000000051 Guyon, 2002, Gene selection for cancer classification using support vector machines, Machine Learning, 46, 389, 10.1023/A:1012487302797 Hand, 1981 Hand, 2006, Classifier technology and the illusion of progress, Statistical Science, 21, 1, 10.1214/088342306000000060 Henley, 1997, Construction of a k-nearest-neighbour credit-scoring system, Journal of Management Mathematics, 8, 305 Huang, 2004, Credit rating analysis with support vector machines and neural networks: A market comparative study [Special issue: Data mining for financial decision making], Decision Support Systems, 37, 543, 10.1016/S0167-9236(03)00086-1 Huang, 2007, Credit scoring with a data mining approach based on support vector machines, Expert Systems with Applications, 33, 847, 10.1016/j.eswa.2006.07.007 Joachims, 1999, Making large-scale SVM learning practical Lee, 2007, Application of support vector machines to corporate credit rating prediction, Expert Systems with Applications, 33, 67, 10.1016/j.eswa.2006.04.018 Li, 2006, The evaluation of consumer loans using support vector machines, Expert Systems with Applications, 30, 772, 10.1016/j.eswa.2005.07.041 Olsson, J. S. (2006). An analysis of the coupling between training set and neighbourhood sizes for the kNN classifier. In Proceedings of the 29th annual international ACM SIGIR. Schebesch, 2005, Support vector machines for classifying and describing credit applicants: Detecting typical and critical regions, Journal of the Operational Research Society, 56, 1082, 10.1057/palgrave.jors.2602023 Thomas, 2002 Van Gestel, 2006, Bayesian kernel based classification for financial distress detection, European Journal of Operational Research, 172, 979, 10.1016/j.ejor.2004.11.009 Vapnik, 1998