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

作者：Zhu, Fangdong; Chen, Wen^*; Yang, Hanli^*; Li, Tao; Yang, Tao; Zhang, Fan

来源：Mathematical Problems in Engineering, 2017, 2017: 3956415.

DOI：10.1155/2017/3956415

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 underMap/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.

• 出版日期2017
• 单位成都信息工程大学; 重庆理工大学; 四川大学