Induction of fuzzy classification rules with an artificial immune system

Alves, Roberto T. and Delgado, Myriam and Lopes, Heitor S. and Freitas, Alex A. (2004) Induction of fuzzy classification rules with an artificial immune system. In: Barros, A. and Araujo, A. and Yehia, H.C. and Teixeira, R., eds. Proceedings of the 8th Brazillian Symposium on Neural Networks. IEEE. ISBN 85-89029-04-2. (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) (KAR id:14055)

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.

Abstract

Fuzzy systems were designed to compute with uncertainties and linguistic information and allow us to develop mathematical tools for information processing. Artificial immune systems (AIS) consist of methods inspired by the biological immune system and designed for solving real-world problems. This work integrates these two kinds of systems, proposing a novel AIS for discovering fuzzy classification rules from data. The results of the proposed algorithm are compared with the results of C4.5Rules, a very popular algorithm for discovering classification rules.

Item Type:	Book section
Uncontrolled keywords:	data mining, artificial immune systems, fuzzy systems, classification
Subjects:	Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions:	Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing
Depositing User:	Mark Wheadon
Date Deposited:	24 Nov 2008 18:01 UTC
Last Modified:	16 Nov 2021 09:52 UTC
Resource URI:	https://kar.kent.ac.uk/id/eprint/14055 (The current URI for this page, for reference purposes)

University of Kent Author Information

Freitas, Alex A..

Creator's ORCID:	https://orcid.org/0000-0001-9825-4700
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