A scalable artificial immune system model for dynamic unsupervised learning

Authors:
Olfa Nasraoui;Fabio Gonzalez;Cesar Cardona;Carlos Rojas;Dipankar Dasgupta
Affiliations:
Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN;The University of Memphis, Memphis, TN;Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN;Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN;The University of Memphis, Memphis, TN
Venue:
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Year:
2003

Citing 5
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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.