A Scalable Artificial Immune System Model for Dynamic Unsupervised Learning

Olfa Nasraoui¹, Fabio Gonzalez², Cesar Cardona¹, Carlos Rojas¹, and Dipankar Dasgupta²

¹Department of Electrical and Computer Engineering,
The University of Memphis
Memphis, TN 38152
{onasraou,ccardona,crojas}@memphis.edu

²Division of Computer Sciences,
The University of Memphis
Memphis, TN 38152
{fgonzalz,ddasgupt}@memphis.edu

Abstract. Artificial Immune System (AIS) models offer a promising approach to data analysis and pattern recognition. However, in order to achieve a desired learning capability (for example detecting all clusters in a dat set), current models require the storage and manipulation of a large network of B Cells (with a number often exceeding the number of data points in addition to all the pairwise links between these B Cells). Hence, current AIS models are far from being scalable, which makes them of limited use, even for medium size data sets.

We propose a new scalable AIS learning approach that exhibits superior learning abilities, while at the same time, requiring modest memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to current approaches is expected to be its ease of adaptation in dynamic environments. We illustrate the ability of the proposed approach in detecting clusters in noisy data.

Keywords. Artificial immune systems, scalability, clustering, evolutionary computation, dynamic learning

LNCS 2723, p. 219 ff.

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