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

I. Edhem Sakarya and O. Erhun Kundakcioglu ()
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I. Edhem Sakarya: Eindhoven University of Technology
O. Erhun Kundakcioglu: Ozyegin University

Journal of Global Optimization, 2023, vol. 85, issue 2, No 4, 375 pages

Abstract: 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.

Keywords: Multi-instance learning; Mixed integer linear programming; Area under curve (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10898-022-01219-y

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