One-Class Classification by Ensembles of Random Planes (OCCERPs)

Abstract

One-class classification (OCC) deals with the classification problem in which the training data have data points belonging only to the target class. In this paper, we present a one-class classification algorithm, One-Class Classification by Ensembles of Random Plane (OCCERP), that uses random planes to address OCC problems. OCCERP creates many random planes. There is a pivot point in each random plane. A data point is projected in a random plane and a distance from a pivot point is used to compute the outlier score of the data point. Outlier scores of a point computed using many random planes are combined to get the final outlier score of the point. An extensive comparison of the OCCERP algorithm with state-of-the-art OCC algorithms on several datasets was conducted to show the effectiveness of the proposed approach. The effect of the ensemble size on the performance of the OCCERP algorithm is also studied.