A supervised classifier based on artificial immune system

Abstract

Artificial immune recognition system (AIRS) has been convincingly proved a highly effective classifier, which has been successfully applied to pattern recognition and etc. However, there are two shortcomings that limit its further applications, one is the huge size of evolved memory cells pool, and the other is low classification accuracy. In order to overcome these limitations, a supervised artificial immune classifier, UCAIS, is presented. The implementation of UCAIS includes: the first is to create a pool of memory cells. Then, B-cell population is evolved and the memory cells pool is updated until the stopping criterion is met. Finally, classification is accomplished by majority vote of the k nearest memory cells. Compared with AIRS, UCAIS not only reduces the huge size of evolved memory cells pool, but also improves the classification accuracy on the four famous datasets, the Iris dataset, the Ionosphere dataset, the Diabetes dataset, and the Sonar dataset. © Springer-Verlag Berlin Heidelberg 2007.