A fuzzy logic resource allocation and memory cell pruning based artificial immune recognition system

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.