TY  - JOUR
AU  - Pierluigi Casale
AU  - Oriol Pujol
AU  - Petia Radeva
PY  - 2014//
TI  - Approximate polytope ensemble for one-class classification
T2  - PR
JO  - Pattern Recognition
SP  - 854
EP  - 864
VL  - 47
IS  - 2
KW  - One-class classification
KW  - Convex hull
KW  - High-dimensionality
KW  - Random projections
KW  - Ensemble learning
N2  - 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.
UR  - http://dx.doi.org/10.1016/j.patcog.2013.08.007
N1  - MILAB; 605.203
ID  - Pierluigi Casale2014
ER  -