Feature Selection for Financial Credit-Risk Evaluation Decisions

Abstract

Credit-risk evaluation decisions are important for the financial institutions involved due to the high level of risk associated with wrong decisions. Various machine learning methods have been shown to perform reasonably well for this complex and unstructured problem. However, selecting a “good” set of features to be used in any learning system is a hard problem. We survey recent developments in feature selection and propose a new methodology based on the “Blurring” measure. The proposed feature selection method (FSB) is used to preprocess input data for induced decision trees. Three financial credit-risk evaluation data sets are used to illustrate the performance of the proposed method. In addition to FSB, results from randomly selected features, features selected using the Patrick-Fisher probabilistic distance measure, as well as no feature selection are provided for comparison purposes. Given the characteristics of the financial credit-risk evaluation domain, any improvement in classification performance is deemed beneficial. Preliminary results indicate that for comparable tree size, classification performance of FSB on classifying heretofore unseen examples is good compared to the other three methods evaluated in this study.