A fuzzy logic resource allocation and memory cell pruning based artificial immune recognition system
来源期刊：中南大学学报(英文版)2014年第2期
论文作者：DENG Ze-lin(邓泽林) TAN Guan-zheng(谭冠政) HE Pei(何锫) YE Ji-xiang(叶吉祥)
文章页码：610 - 617
Key words：artificial immune recognition system; fuzzy logic; memory cell pruning; classification
Abstract: In order to improve the resource allocation mechanism of artificial immune recognition system (AIRS) and decrease the memory cells, a fuzzy logic resource allocation and memory cell pruning based AIRS (FPAIRS) is proposed. In FPAIRS, the fuzzy logic is determined by a parameter, thus, the optimal fuzzy logics for different problems can be located through changing the parameter value. At the same time, the memory cells of low fitness scores are pruned to improve the classifier. This classifier was compared with other classifiers on six UCI datasets classification performance. The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers, and the memory cells decrease when compared with the memory cells of AIRS. The results show that the algorithm is a high-performance classifier.
DENG Ze-lin(邓泽林)1, 2, TAN Guan-zheng(谭冠政)1, HE Pei(何锫)2, YE Ji-xiang(叶吉祥)2
(1. School of Information Science and Engineering, Central South University, Changsha 410083, China;
2. School of Computer and Communication Engineering,
Changsha University of Science and Technology, Changsha 410076, China)
Abstract:In order to improve the resource allocation mechanism of artificial immune recognition system (AIRS) and decrease the memory cells, a fuzzy logic resource allocation and memory cell pruning based AIRS (FPAIRS) is proposed. In FPAIRS, the fuzzy logic is determined by a parameter, thus, the optimal fuzzy logics for different problems can be located through changing the parameter value. At the same time, the memory cells of low fitness scores are pruned to improve the classifier. This classifier was compared with other classifiers on six UCI datasets classification performance. The results show that the accuracies reached by FPAIRS are higher than or comparable to the accuracies of other classifiers, and the memory cells decrease when compared with the memory cells of AIRS. The results show that the algorithm is a high-performance classifier.
Key words:artificial immune recognition system; fuzzy logic; memory cell pruning; classification
