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Multi-instance learning by maximizing the area under receiver operating characteristic curve

Author

Listed:
  • I. Edhem Sakarya

    (Eindhoven University of Technology)

  • O. Erhun Kundakcioglu

    (Ozyegin University)

Abstract

The purpose of this study is to solve the multi-instance classification problem by maximizing the area under the Receiver Operating Characteristic (ROC) curve obtained for witness instances. We derive a mixed integer linear programming model that chooses witnesses and produces the best possible ROC curve using a linear ranking function for multi-instance classification. The formulation is solved using a commercial mathematical optimization solver as well as a fast metaheuristic approach. When the data is not linearly separable, we illustrate how new features can be generated to tackle the problem. We present a comprehensive computational study to compare our methods against the state-of-the-art approaches in the literature. Our study reveals the success of an optimal linear ranking function through cross validation for several benchmark instances.

Suggested Citation

  • I. Edhem Sakarya & O. Erhun Kundakcioglu, 2023. "Multi-instance learning by maximizing the area under receiver operating characteristic curve," Journal of Global Optimization, Springer, vol. 85(2), pages 351-375, February.
    • Handle: RePEc:spr:jglopt:v:85:y:2023:i:2:d:10.1007_s10898-022-01219-y
    DOI: 10.1007/s10898-022-01219-y
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    References listed on IDEAS

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    1. Mohammad Poursaeidi & O. Kundakcioglu, 2014. "Robust support vector machines for multiple instance learning," Annals of Operations Research, Springer, vol. 216(1), pages 205-227, May.
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