Data mining feature selection for credit scoring models

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'.