A resource limited artificial immune system for data analysis

Timmis, Jon and Neal, Mark (2001) A resource limited artificial immune system for data analysis. In: 20th SGES International Conference on Knowledge Based Systems and Applied Artificial Intelligence (ES2000), Dec 11-13, 2000, Cambridge, England. (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

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This paper presents a resource limited artificial immune system for data analysis. The work presented here builds upon previous work on artificial immune systems for data analysis. A population control mechanism, inspired by the natural immune system, has been introduced to control population growth and allow termination of the learning algorithm. The new algorithm is presented, along with the immunological metaphors used as inspiration. Results are presented for Fisher Iris data set, where very successful results are obtained in identifying clusters within the data set. It is argued that this new resource based mechanism is a large step forward in making artificial immune systems a viable contender for effective unsupervised machine learning and allows for not just a one shot learning mechanism, but a continual learning model to be developed.

Item Type: Conference or workshop item (Paper)
Additional information: Issue 3-4; Sp. Iss. SI
Uncontrolled keywords: artificial immune system, machine learning, immune metaphor
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 17:59
Last Modified: 21 May 2011 00:34
Resource URI: https://kar.kent.ac.uk/id/eprint/13601 (The current URI for this page, for reference purposes)
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