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Multi-instance classification through spherical separation and VNS
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DOI：10.1016/j.cor.2013.05.009.png)
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Abstract
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A two-class classification problem is considered where the objects to be classified are bags of instances in d-space. The classification rule is defined in terms of an open d-ball. A bag is labeled positive if it meets the ball and labeled negative otherwise. Determining the center and radius of the ball is modeled as a SVM-like margin optimization problem. Necessary optimality conditions are derived leading to a polynomial algorithm in fixed dimension. A VNS type heuristic is developed and experimentally tested. The methodology is extended to classification by several balls and to more than two classes. (C) 2013 Elsevier Ltd. All rights reserved.
Keywords:
Supervised classification
Multi-instance learning
Mixed-integer programming
Variable neighborhood search
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