  


Building separating concentric balls to solve a multiinstance classification problem
Emilio Carrizosa (ecarrizosaus.es) Abstract: In this work, we consider a classification problem where the objects to be classified are bags of instances which are vectors measuring d different attributes. The classification rule is defined in terms of a ball, whose center and radius are the parameters to be computed. Given a bag, it is assigned to the positive class if at least one element is strictly included inside the ball, and it is labelled as negative otherwise. We model this question as a margin optimization problem. Several necessary optimality conditions are derived leading to a polynomial algorithm in fixed dimension. A VNS type heuristic is proposed and experimentally tested. Keywords: Supervised Classification, MultiInstance Learning, MixedInteger Programming, Variable Neighbourhood Search Category 1: Applications  Science and Engineering (DataMining ) Category 2: Integer Programming ((Mixed) Integer Nonlinear Programming ) Category 3: Combinatorial Optimization (Meta Heuristics ) Citation: Download: [PDF] Entry Submitted: 01/31/2008 Modify/Update this entry  
Visitors  Authors  More about us  Links  
Subscribe, Unsubscribe Digest Archive Search, Browse the Repository

Submit Update Policies 
Coordinator's Board Classification Scheme Credits Give us feedback 
Optimization Journals, Sites, Societies  