Memory Cell Pruning and Nonlinear Resource Allocation BasedArtificial Immune Recognition System
DENG Ze-Lin1,2 ,TAN Guan-Zheng1,HE Pei2
1. School of Information Science and Engineering,Central South University,Changsha 410083 2.School of Computer and Communication Engineering,Changsha University of Science and Technology,Changsha 410076
Abstract To reduce the memory cells of artificial immune recognition system (AIRS) and improve AIRS classification performance,a memory cell pruning and nonlinear resource allocation based artificial immune recognition system (PNAIRS) is proposed. Attribute discretization pre-processing is adopted to compress the training space. Memory cell pruning operation is employed to eliminate the memory cells of low fitness scores,and nonlinear resource allocation is utilized to optimize the classifier. PNAIRS is applied to 6 UCI datasets classification,the classification performance is compared with other classifiers. PNAIRS generates small memory cell population and reaches high classification accuracy,and the classification is finished quickly. The results show that PNAIRS is a high-performance classifier,and it has potential application.
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2013, Vol. 26 
