Approximate polytope ensemble for one-class classification

•The methodology uses a convex-hull for modeling one-class classification problems.•Random projections are used to approximate the convex-hull in high dimensional spaces.•Expansions of the approximate hulls are considered to set the optimal operating point.•Exhaustive validation is performed on three different typologies of problems.

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.