A resource limited artificial immune system for data analysis

J. Timmis*, M. Neal

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

272 Citations (SciVal)

Abstract

This paper presents a resource limited artificial immune system (RLAIS) for data analysis. The work presented here builds upon previous work on artificial immune systems (AIS) 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 AISs 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.

Original languageEnglish
Pages (from-to)121-130
Number of pages10
JournalKnowledge-Based Systems
Volume14
Issue number3-4
Early online date22 May 2001
DOIs
Publication statusPublished - Jun 2001

Keywords

  • Artificial immune system
  • B cell
  • Data analysis
  • Kohonen networks
  • Machine learning

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