A maximum-margin multisphere approach for binary Multiple Instance Learning

Annabella Astorino, Matteo Avolio and Antonio Fuduli

European Journal of Operational Research, 2022, vol. 299, issue 2, 642-652

Abstract: We propose a heuristic approach for solving binary Multiple Instance Learning (MIL) problems, whose objective is to categorize bags of instances. Considering the case with two classes of instances, on the basis of the standard MIL assumption, a bag is classified positive if it contains at least a positive instance and negative if all its instances are negative. Inspired by a well-established MIL Support Vector Machine type approach, our technique is based on iteratively separating the bags by means of successive maximum-margin spheres. Such spheres, whose number is automatically determined, are generated by computing, for each of them, the optimal radius in correspondence to a prefixed center. Numerical results are presented on a set of benchmark test problems, showing the effectiveness of our approach.

Keywords: Machine learning; Multiple Instance Learning; Spherical separation; Fixed-center margin maximization (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:299:y:2022:i:2:p:642-652

DOI: 10.1016/j.ejor.2021.11.022

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