Revisiting the foundations of artificial immune systems: A problem-oriented perspective

Alex A. Freitas*, Jon Timmis

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

70 Citations (Scopus)

Abstract

Since their development, AIS have been used for a number of machine learning tasks including that of classification. Within the literature, there appears to be a lack of appreciation for the possible bias in the selection of various representations and affinity measures that may be introduced when employing AIS in classification tasks. Problems are then compounded when inductive bias of algorithms are not taken into account when applying seemingly generic AIS algorithms to specific application domains. This paper is an attempt at highlighting some of these issues. Using the example of classification, this paper explains the potential pitfalls in representation selection and the use of various affinity measures. Additionally, attention is given to the use of negative selection in classification and it is argued that this may be not an appropriate algorithm for such a task. This paper then presents ideas on avoiding unnecessary mistakes in the choice and design of AIS algorithms and ultimately delivered solutions.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsJon Timmis, Peter Bentley, Emma Hart
PublisherSpringer Nature
Pages229-241
Number of pages13
ISBN (Print)3540407669, 9783540407669
DOIs
Publication statusPublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2787
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Keywords

  • classification task
  • classification agorithm
  • artificial immune system
  • categorical attribute
  • distance metrics

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