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Solving Credit Card Fraud Detection Problem by the New Metaheuristics Migrating Birds Optimization

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7903)

Abstract

Statistical fraud detection problem is a very difficult problem in that there are very few examples of fraud. The great majority of transactions are legitimate. On the other hand, for this binary classification problem the costs of the two types of classification errors (FP=false positive and FN=false negative) are not the same. Thus, the classical data mining algorithms do not fit to the problem exactly. Departing from this fact, we have solved this problem by genetic algorithms and scatter search. Now, we apply the recently developed new metaheuristics algorithm namely the migrating birds optimization algorithm (MBO) to this problem. Results show that it outperforms the former approach. The performance of standard MBO is further increased by the help of some modified benefit mechanisms.

Keywords

migrating birds optimization algorithm fraud credit cards genetic algorithms 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Faculty of Engineering, Industrial Engineering DepartmentÖzyeğin UniversityIstanbulTurkey
  2. 2.Decision Support Systems DepartmentIntertechIstanbulTurkey

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