One-Class Convex Hull-Based Algorithm for Classification in Distributed Environments

Abstract

In this paper, a new one-class classification algorithm capable of working in distributed environments is presented. In it, convex hull is used to build the boundary of the target class defining the one-class problem in each of the distributed nodes. Therefore, we will consider several classifiers, each one determined using a given local data partition, and the goal is to obtain a global classification decision. In order to obtain this final decision, two different algebraic combination rules were proposed: 1) sum and 2) majority voting. Experimental results show that this method opens the possibility of tackling practical one-class classification problems in distributed big data scenarios in an efficient and accurate way.