Abstract
Various machine learning techniques have been explored for credit scoring and management, but no consistent conclusions have been drawn on which method shows the best behaviour. This paper presents an experimental analysis involving five real-world databases with several credit scoring models, including logistic regression, neural networks, support vector machines, decision trees, rule induction algorithms, Bayesian models, k nearest neighbours decision rule, and classifier ensembles. Particularly, we analyse the performance of this set of algorithms by means of a non-parametric statistical test and two post-hoc procedures for making pairwise comparisons.
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García, V., Marqués, A.I., Sánchez, J.S. (2012). Non-parametric Statistical Analysis of Machine Learning Methods for Credit Scoring. In: Casillas, J., Martínez-López, F., Corchado Rodríguez, J. (eds) Management Intelligent Systems. Advances in Intelligent Systems and Computing, vol 171. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30864-2_25
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DOI: https://doi.org/10.1007/978-3-642-30864-2_25
Publisher Name: Springer, Berlin, Heidelberg
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