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SVM classification for imbalanced data sets using a multiobjective optimization framework

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  • Ayşegül Aşkan
  • Serpil Sayın

Abstract

Classification of imbalanced data sets in which negative instances outnumber the positive instances is a significant challenge. These data sets are commonly encountered in real-life problems. However, performance of well-known classifiers is limited in such cases. Various solution approaches have been proposed for the class imbalance problem using either data-level or algorithm-level modifications. Support Vector Machines (SVMs) that have a solid theoretical background also encounter a dramatic decrease in performance when the data distribution is imbalanced. In this study, we propose an L 1 -norm SVM approach that is based on a three objective optimization problem so as to incorporate into the formulation the error sums for the two classes independently. Motivated by the inherent multi objective nature of the SVMs, the solution approach utilizes a reduction into two criteria formulations and investigates the efficient frontier systematically. The results indicate that a comprehensive treatment of distinct positive and negative error levels may lead to performance improvements that have varying degrees of increased computational effort. Copyright Springer Science+Business Media New York 2014

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  • Ayşegül Aşkan & Serpil Sayın, 2014. "SVM classification for imbalanced data sets using a multiobjective optimization framework," Annals of Operations Research, Springer, vol. 216(1), pages 191-203, May.
    • Handle: RePEc:spr:annopr:v:216:y:2014:i:1:p:191-203:10.1007/s10479-012-1300-5
    DOI: 10.1007/s10479-012-1300-5
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    References listed on IDEAS

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    1. Panos Kouvelis & Serpil Sayın, 2006. "Algorithm robust for the bicriteria discrete optimization problem," Annals of Operations Research, Springer, vol. 147(1), pages 71-85, October.
    2. Shuchun Wang & Wei Jiang & Kwok-Leung Tsui, 2010. "Adjusted support vector machines based on a new loss function," Annals of Operations Research, Springer, vol. 174(1), pages 83-101, February.
    3. Fruhwirth, Bernd & Mekelburg, Karsten, 1994. "On the efficient point set of tricriteria linear programs," European Journal of Operational Research, Elsevier, vol. 72(1), pages 192-199, January.
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