Immune inspired information filtering in a high dimensional space

Abstract

Adaptive Information Filtering is a challenging computational problem that requires a high dimensional feature space. However, theoretical issues arise when vector-based representations are adopted in such a space. In this paper, we use AIF as a test bed to provide experimental evidence indicating that the learning abilities of vector-based Artificial Immune Systems are diminished in a high dimensional space. © 2010 Springer-Verlag Berlin Heidelberg.