Revisiting the Foundations of Artificial Immune Systems: A Problem Oriented Perspective

Freitas, Alex A. and Timmis, Jon (2003) Revisiting the Foundations of Artificial Immune Systems: A Problem Oriented Perspective. In: Proceedings of the 2nd International Conference on Artificial Immune Systems. (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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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.

Item Type: Conference or workshop item (UNSPECIFIED)
Uncontrolled keywords: artificial immune systems, data mining, classification, representation
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 18:00
Last Modified: 17 Jun 2014 12:56
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