Virus Evolution Based for Classification Rules Mining

Virus Evolution Based Gene Expression Programming for Classification Rules Mining
Journal	Key Engineering Materials (Volumes 467 - 469)
Volume	Materials, Mechatronics and Automation
Edited by	Dehuai Zeng
Pages	1392-1397
DOI	10.4028/www.scientific.net/KEM.467-469.1392
Citation	Wei Hong Wang et al., 2011, Key Engineering Materials, 467-469, 1392
Online since	February, 2011
Authors	Wei Hong Wang, Yan Ye Du, Qu Li
Keywords	Classification, Gene Expression Programming, Virus Evolution
Abstract	Gene Expression Programming(GEP) is a novel and accurate approach for classification. With the shortcoming of GEP, it often falls into the local optimums. In this paper, we introduce the virus evolutionary mechanism into GEP, with the infection operation of virus population, the diversity of the host population is increased, and the system is much easier to jump out of the local optimums, and much faster to obtain better results. Experiments on several benchmark data sets show that our approach can get close average accuracy and much better best accuracy compared with available results. What’s more, the average execution time is largely decreased due to smaller population size and maximum generation.
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http://www.scientific.net/KEM.467-469.1392

Virus Evolution Based for Classification Rules Mining