One-class classification algorithm based on convex hull

A new version of a one-class classification algorithm is presented in this paper. In it, convex hull (CH) is used to define the boundary of the target class defining the one-class problem. An approximation of the D-dimensional CH decision is made by using random projections and an ensemble of models in very low-dimensional spaces. Expansion and reduction of the CH model prevents over-fitting. So a different method to obtain the expanded polytope is proposed in order to avoid some undesirable behavior detected in the original algorithm in certain situations. Besides, this modification allows the use of a new parameter, the CH center, that provides even more flexibility to our proposal. Experimental results showed that the new algorithm is significantly better, regarding accuracy, than the previous work on a large number of datasets.