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Title:	Approximate polytope ensemble for one-class classification
Author:	Casale, Pierluigi; Pujol Vila, Oriol; Radeva, Petia
Other authors:	Universitat de Barcelona
Abstract:	In this work, a new one-class classification ensemble strategy called approximate polytope ensemble is presented. The main contribution of the paper is threefold. First, the geometrical concept of convex hull is used to define the boundary of the target class defining the problem. Expansions and contractions of this geometrical structure are introduced in order to avoid over-fitting. Second, the decision whether a point belongs to the convex hull model in high dimensional spaces is approximated by means of random projections and an ensemble decision process. Finally, a tiling strategy is proposed in order to model non-convex structures. Experimental results show that the proposed strategy is significantly better than state of the art one-class classification methods on over 200 datasets.
Subject(s):	-Algorismes computacionals -Processament digital d'imatges -Geometria convexa -Computer algorithms -Digital image processing -Convex geometry
Rights:	(c) Elsevier Ltd, 2014
Document type:	Article Article - Accepted version
Published by:	Elsevier Ltd
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