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One-Class Convex Hull-Based Algorithm for Classification in Distributed Environments
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DOI：10.1109/TSMC.2017.2771341.png)
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Abstract
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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.
Keywords:
Distributed databases
Training
Data models
Partitioning algorithms
Testing
Current measurement
Big data
convex hull (CH)
distributed learning
one-class classification
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