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Evolutionary Intelligence
Erschienen in: Evolutionary Intelligence 2/2012

01.06.2012 | Special Issue

On XCSR for electronic fraud detection

verfasst von: Mohammad Behdad, Luigi Barone, Tim French, Mohammed Bennamoun

Erschienen in: Evolutionary Intelligence | Ausgabe 2/2012

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Abstract

Fraud is a serious problem that costs the worldwide economy billions of dollars annually. However, fraud detection is difficult as perpetrators actively attempt to mask their actions, among typically overwhelming large volumes of, legitimate activity. In this paper, we investigate the fraud detection problem and examine how learning classifier systems can be applied to it. We describe the common properties of fraud, introducing an abstract problem which can be tuned to exhibit those characteristics. We report experiments on this abstract problem with a popular real-time learning classifier system algorithm; results from our experiments demonstrating that this approach can overcome the difficulties inherent to the fraud detection problem. Finally we apply the algorithm to a real-world problem and show that it can achieve good performance in this domain.
Vorheriger Artikel On principal component analysis for high-dimensional XCSR

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Metadaten
Titel
On XCSR for electronic fraud detection
verfasst von
Mohammad Behdad
Luigi Barone
Tim French
Mohammed Bennamoun
Publikationsdatum
01.06.2012
Verlag
Springer-Verlag
Erschienen in
Evolutionary Intelligence / Ausgabe 2/2012
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI
https://doi.org/10.1007/s12065-012-0076-5

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