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A resource limited artificial immune system for data analysis

Timmis, Jon, Neal, Mark (2001) A resource limited artificial immune system for data analysis. Knowledge Based Systems, 14 (3-4). pp. 121-130. ISSN 0950-7051. (doi:10.1016/S0950-7051(01)00088-0) (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) (KAR id:13601)

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. (Contact us about this Publication)
Official URL
http://dx.doi.org/10.1016/S0950-7051(01)00088-0

Abstract

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: Article
DOI/Identification number: 10.1016/S0950-7051(01)00088-0
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: Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 17:59 UTC
Last Modified: 16 Feb 2021 12:24 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/13601 (The current URI for this page, for reference purposes)
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