NKA: a pathogen dose-based natural killer cell algorithm and its application to classification

Inspired by the self/non-self discrimination theory, Forrest et al. proposed a distance-based negative selection algorithm (NSA). However, in the detector generation phase, NSA creates specific antibodies for each antigen based on the mechanism of specific antigen–antibody binding reactions, which lead to an excessive and redundant number of detectors. Moreover, during the detection phase, NSA needs to calculate the matching degree of the antigen with all the detectors, which has a high computational cost and low detection efficiency. This letter presents a novel natural killer cell algorithm (NKA) inspired by the mechanism of NK cells constructing phenotype detectors based on pathogen dose, which is a non-specific immune mechanism. NKA defines dose and phenotype detector, optimizes the detectors based on the memory evolution mechanism of phenotype, then establishes k-d tree for the optimized phenotype, and pathogens only need to match the dose with the nearest phenotype detector. Experimental results reveal that NKA can not only achieve the best performance through fewer detectors but also has a higher efficiency in the training and detection phase, compared with three versions of the distance-based NSA algorithms. Meanwhile, NKA generates comparable results compared with six popular machine learning algorithms.