Secker, A. and Freitas, A.A. and Timmis, Jon (2005) Towards a danger theory inspired artificial immune system for web mining. In: Scime, A., ed. Web Mining: applications and techniques. Idea Group, pp. 145-168. ISBN 1591404150.
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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 Email 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.
|Item Type:||Book section|
|Uncontrolled keywords:||artificial immune systems, web mining, data mining, e-mail classification|
|Subjects:||Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,|
|Divisions:||Faculties > Science Technology and Medical Studies > School of Computing > Applied and Interdisciplinary Informatics Group|
|Depositing User:||Mark Wheadon|
|Date Deposited:||24 Nov 2008 18:03|
|Last Modified:||08 Jun 2012 13:44|
|Resource URI:||http://kar.kent.ac.uk/id/eprint/14372 (The current URI for this page, for reference purposes)|
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