Please use this identifier to cite or link to this item:
Title: Approximate polytope ensemble for one-class classification
Author: Casale, Pierluigi
Pujol Vila, Oriol
Radeva, Petia
Keywords: Algorismes computacionals
Processament digital d'imatges
Geometria convexa
Computer algorithms
Digital image processing
Convex geometry
Issue Date: Feb-2014
Publisher: Elsevier Ltd
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.
Note: Versió postprint del document publicat a:
It is part of: Pattern Recognition, 2014, vol. 47, num. 2, p. 854-864
Related resource:
ISSN: 0031-3203
Appears in Collections:Articles publicats en revistes (Matemàtiques i Informàtica)
Publicacions de projectes de recerca finançats per la UE

Files in This Item:
File Description SizeFormat 
638858.pdf1.25 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.