School of Computing

A Resource Limited Artificial Immune System for Data Analysis

J. Timmis and M. J. Neal

Research and Development in Intelligent Systems XVII, pages 182-196, December 2000 Proceedings of ES2000, Cambridge, UK.

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.



Bibtex Record

@article{1121,
author = {J. Timmis and M. J. Neal},
title = {{A Resource Limited Artificial Immune System for Data Analysis}},
month = {December},
year = {2000},
pages = {182-196},
keywords = {determinacy analysis, Craig interpolants},
note = {Proceedings of ES2000, Cambridge, UK.},
doi = {},
url = {http://www.cs.kent.ac.uk/pubs/2000/1121},
    ISBN = {1-85233-403-7},
    journal = {Research and Development in Intelligent Systems XVII},
    publication_type = {article},
    publisher = {Springer},
    submission_id = {14984_972377492},
}

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Last Updated: 21/03/2014