Title: Comparing a genetic fuzzy and a neurofuzzy classifier for credit scoring
Authors: Hoffmann, F ×
Baesens, Bart
Martens, Jurgen
Put, Ferdinand
Vanthienen, Jan #
Issue Date: 2002
Publisher: John Wiley & Sons
Series Title: International Journal of Intelligent Systems vol:17 issue:11 pages:1067-1083
Abstract: In this paper, we evaluate and contrast two types of fuzzy classifiers for credit scoring. The first classifier uses evolutionary optimization and boosting for learning fuzzy classification rules. The second classifier is a fuzzy neural network that employs a fuzzy variant of the classic backpropagation learning algorithm. The experiments are carried out on a real life credit scoring data set. It is shown that, for the case at hand, the boosted genetic fuzzy classifier performs better than both the neurofuzzy classifier and the well-known C4.5(rules) decision tree(rules) induction algorithm. However, the better performance of the genetic fuzzy classifier is offset by the fact that it infers approximate fuzzy rules which are less comprehensible for humans than the descriptive fuzzy rules inferred by the neurofuzzy classifier. (C) 2002 Wiley Periodicals, Inc.
ISSN: 0884-8173
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS), Leuven
× corresponding author
# (joint) last author

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