IngentaConnect Data mining feature selection for credit scoring models

Authors: Liu, Y¹; Schumann, M¹

Source: Journal of the Operational Research Society, Volume 56, Number 9, September 2005 , pp. 1099-1108(10)

Abstract:

The features used may have an important effect on the performance of credit scoring models. The process of choosing the best set of features for credit scoring models is usually unsystematic and dominated by somewhat arbitrary trial. This paper presents an empirical study of four machine learning feature selection methods. These methods provide an automatic data mining technique for reducing the feature space. The study illustrates how four feature selection methods—‘ReliefF’, ‘Correlation-based’, ‘Consistency-based’ and ‘Wrapper’ algorithms help to improve three aspects of the performance of scoring models: model simplicity, model speed and model accuracy. The experiments are conducted on real data sets using four classification algorithms—‘model tree (M5)’, ‘neural network (multi-layer perceptron with back-propagation)’, ‘logistic regression’, and ‘k-nearest-neighbours’.Journal of the Operational Research Society (2005) 56, 1099–1108. doi:10.1057/palgrave.jors.2601976 Published online 20 April 2005

Document Type: Research article

DOI: 10.1057/palgrave.jors.2601976

Affiliations: 1: 1University of Goettingen, Germany

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