The Dual Negative Selection Algorithm Based on Pattern Recognition Receptor Theory and Its Application in Two-class Data Classification

Negative Selection Algorithm (NSA) is an important artificial immune data classifiers generation method in Artificial Immune System (AIS) research. However, with the increase of the data dimensions, the current data classification algorithms which based on NSA exist the problems of excessive number of generated classifiers and too low classifier generation efficiency. In this paper, the Dual Negative Selection Algorithm based on Pattern Recognition Receptor theory (PRR-2NSA) is proposed, which simulates the process of Antigen Presenting Cells (APC) recognized the Pathogen-Associated Molecular Patterns (PAMP) to trigger the immune response. The PRR-2NSA algorithm generates the APC classifier based on training set clustering firstly, and then generates the T-cell classifiers within the coverage of the APC classifier set with dual negative selection algorithm (2NSA) secondly. The 2NSA avoids the unnecessary and time-consuming self-tolerance process of candidate classifier within the coverage of existing mature classifiers, thus greatly reduces classifier set size, significantly improves classifier generation efficiency. The PRR-2NSA introduces the APC classifiers’ co-stimulation to the T-Cell classifier, which reduce the occurrence of false classification on one hand, and accelerate the data classification efficiency on the other hand. Theoretical analysis and simulations show that the PRR-2NSA algorithm effectively improves classification efficiency and reduces the time cost of algorithm.