Approximate polytope ensemble for one-class classification

PATTERN RECOGNITION

卷:47

Article

Casale, Pierluigi¹  Pujol, Oriol²  Radeva, Petia² 

[1] TU Eindhoven, Signal Proc Grp, ACTLab, NL-5612 AZ Eindhoven, Netherlands

[2] Univ Barcelona, Dept Matemat Aplicada & Anal, E-08007 Barcelona, Spain

关键词: One-class classification;    Convex hull;    High-dimensionality;    Random projections;    Ensemble learning;   

DOI  :  10.1016/j.patcog.2013.08.007

来源: Elsevier

【 摘 要 】

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. (C) 2013 Elsevier Ltd. All rights reserved.

【 授权许可】

Free