Novel negative selection algorithm for multiclass pattern classification problems

Abstract

A negative selection algorithm for multi-class pattern classification problems named CS-NSA was proposed.The algorithm used clonal selection mechanism to implement self-adaptive learning of detectors and adopted detector trimming mechanism to tackle the over-fitting problem in multi-class classification.This mechanism enhanced the generalization capability of the detectors.The results of comparative experiments show that the proposed algorithm exhibits higher classifying accuracy than that of AIRS,a famous artificial immune classifier.