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Filter‐ versus wrapper‐based feature selection for credit scoring
Author(s) -
Somol Petr,
Baesens Bart,
Pudil Pavel,
Vanthienen Jan
Publication year - 2005
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.20103
Subject(s) - feature selection , computer science , selection (genetic algorithm) , feature (linguistics) , artificial intelligence , loan , subspace topology , filter (signal processing) , machine learning , data mining , pattern recognition (psychology) , finance , philosophy , linguistics , computer vision , economics
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.