Feature selection based on machine learning for credit scoring : An evaluation of filter and embedded methods

Abstract

Feature Selection (FS) is one of the power solutions used in Machine Learning (ML) problems, since it can help to remove irrelevant and redundant attributes, improve the performance, reduce computation time and build more robust models. In this work, a thorough study is carried out to examine the effect of well-performing filter and embedded FS methods for credit scoring. Further, we explore the effect of such methods on the prediction models obtained using different classification techniques. We conduct our experiments on the Australian credit dataset and the obtained experimental results show the benefits of the proposed methodology in credit risk analysis. By considering the selected FS methods with some well chosen classifiers, they make the evaluation more quickly and increase the accuracy of the classification.