A new classifier based on resource limited artificial immune systems

Authors:
A. Watkins;L. Boggess
Affiliations:
Comput. Lab., Kent Univ., Canterbury, UK;Dept. of Comput. Sci., Brigham Young Univ., Provo, UT, USA
Venue:
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Year:
2002

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Abstract

This paper presents a new tool for supervised learning, modeled on resource limited Artificial Immune Systems. A supervised learning system, it is self-regulatory, efficient, and stable under a wide range of user-set parameters. Its performance is comparable to well-established classifiers on a variety of testbeds, including the iris data, the diabetes classification problem, the ionosphere problem, and the rock/metal classification problem for mine detection.