Filter- versus wrapper-based feature selection for credit scoring

• Published in: International journal of intelligent systems Vol. 20; no. 10; pp. 985 - 999
• Main Authors: Somol, Petr, Baesens, Bart, Pudil, Pavel, Vanthienen, Jan
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.10.2005; Wiley
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Decision theory. Utility theory; Exact sciences and technology; Learning and adaptive systems; Logical, boolean and switching functions; Operational research and scientific management; Operational research. Management science; Theoretical computing; Selection problem; Loans; Wrapper; Loan; Vector space; Filter; Scoring credit; Design feature; Classification; Information extraction; Classifier
• ISSN: 0884-8173; 1098-111X
• DOI: 10.1002/int.20103

We address the problem of credit scoring as a classification and feature subset selection problem. Based on the current framework of sophisticated feature selection methods, we identify features that contain the most relevant information to distinguish good loan payers from bad loan payers. The feature selection methods are validated on several real‐world datasets with different types of classifiers. We show the advantages following from using the subspace approach to classification. We discuss many practical issues related to the applicability of feature selection methods. We show and discuss some difficulties that used to be insufficiently emphasized in standard feature selection literature. © 2005 Wiley Periodicals, Inc. Int J Int Syst 20: 985–999, 2005.