On AIRS and Clonal Selection for Machine Learning

AIRS is an immune-inspired supervised learning algorithm that has been shown to perform competitively on some common datasets. Previous analysis of the algorithm consists almost exclusively of empirical benchmarks and the reason for its success remains somewhat speculative. In this paper, we decouple the statistical and immunological aspects of AIRS and consider their merits individually. This perspective allows us to clarifying why AIRS performs as it does and identify deficiencies that leave AIRS lacking. A comparison with Radial Basis Functions suggests that each may have something to offer the other.