Immune Inspired Information Filtering in a High Dimensional Space
edited by: Emma Hart, Chris McEwan, Jon Timmis, Andy Hone
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.