Non-Euclidean distance measures in AIRS, an artificial immune classification system

Abstract

The AIRS classifier, based on principles derived from resource limited artificial immune systems, performs consistently well over a broad range of classification problems. This paper explores the effects of adding nonEuclidean distance measures to the basic AIRS algorithm using four well-known publicly available classification problems having various proportions of real, discrete, and nominal features.