Towards a Danger Theory Inspired Artificial Immune System for Web Mining

Towards a Danger Theory Inspired Artificial Immune System for Web Mining

Andrew Secker (University of Kent, UK), Alex A. Freitas (University of Kent, UK) and Jon Timmis (University of Kent, UK)
Copyright: © 2005 |Pages: 24
DOI: 10.4018/978-1-59140-414-9.ch007
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Abstract

The natural immune system exhibits many properties that are of interest to the area of Web mining. Of particular interest is the dynamic nature of the immune system when compared with the dynamic nature of mining information from the Web. As part of a larger project to construct a large-scale dynamic Web-mining system, this chapter reports initial work on constructing an e-mail classifier system. The Artificial Immune System for e-mail Classification (AISEC) is described in detail and compared with a traditional approach of naive Bayesian classification. Results reported compare favorably with the Bayesian approach and this chapter highlights how the Danger Theory from immunology can be used to further improve the performance of such an artificial immune system.

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