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Mathematical Problems in Engineering
Volume 2017 (2017), Article ID 3956415, 7 pages
https://doi.org/10.1155/2017/3956415
Research Article

A Quick Negative Selection Algorithm for One-Class Classification in Big Data Era

1College of Computer Science, Sichuan University, Chengdu 610065, China
2College of Cybersecurity, Sichuan University, Chengdu 610065, China
3Chongqing University of Technology, Chongqing 400054, China
4Chengdu University of Information Technology, Chengdu 610225, China

Correspondence should be addressed to Wen Chen; nc.ude.ucs@nehcnew and Hanli Yang; nc.ude.tuqc@lhy

Received 2 February 2017; Accepted 3 May 2017; Published 12 June 2017

Academic Editor: Zonghua Zhang

Copyright © 2017 Fangdong Zhu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Negative selection algorithm (NSA) is an important kind of the one-class classification model, but it is limited in the big data era due to its low efficiency. In this paper, we propose a new NSA based on Voronoi diagrams: VorNSA. The scheme of the detector generation process is changed from the traditional “Random-Discard” model to the “Computing-Designated” model by VorNSA. Furthermore, we present an immune detection process of VorNSA under Map/Reduce framework (VorNSA/MR) to further reduce the time consumption on massive data in the testing stage. Theoretical analyses show that the time complexity of VorNSA decreases from the exponential level to the logarithmic level. Experiments are performed to compare the proposed technique with other NSAs and one-class classifiers. The results show that the time cost of the VorNSA is averagely decreased by 87.5% compared with traditional NSAs in UCI skin dataset.