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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',doi:10.1057/palgrave.jors.2601976 fatcat:k6enizji2zb6nhmhfjrtr4iuli