On the problems of using learning classifier systems for fraud detection

Authors:
Mohammad Behdad;Tim French;Luigi Barone;Mohammed Bennamoun
Affiliations:
The University of Western Australia, Perth, Australia;The University of Western Australia, Perth, Australia;The University of Western Australia, Perth, Australia;The University of Western Australia, Perth, Australia
Venue:
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Year:
2010

Citing 4
Cited 0

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Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta

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Abstract

Fraud detection problems have some uniquely challenging properties which make them difficult. In this paper, we investigate the fraud detection problem by describing the common properties of electronic fraud and examining how learning classifier systems (LCSs) can be applied to it. Also, we introduce "random Boolean function" (RBF); an abstract problem with high level of controllability which can be tuned to exhibit those characteristics individually, and report the results of using XCSR (a continuous variant of LCS) on RBF problem and also on a real-world problem. Results from our experiments demonstrate that XCSR can overcome most of the difficulties inherent to the fraud detection problem and can achieve good performance in case of the real-world problem.