One-Class Convex Hull-Based Algorithm for Classification in Distributed Environments
提出一种能在分布式环境中工作的单类分类算法，利用凸包构建每个节点的目标类边界，并通过求和或多数投票两种组合规则得到全局分类结果，实验表明该方法能高效准确地处理分布式大数据场景下的单类分类问题。
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.