An artificial immune system for fuzzy-rule induction in data mining

Alves, Roberto T. and Delgado, Myriam and Lopes, Heitor S. and Freitas, Alex A. (2004) An artificial immune system for fuzzy-rule induction in data mining. In: Yao, Xin, ed. Parallel Problem Solving from Nature - PPSN VIII. Lecture Notes in Computer Science, 3242. Springer-Verlag pp. 1011-1020. ISBN 3540230920. (doi: 10.1007/b100601) (Full text available)

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This work proposes a classification-rule discovery algorithm integrating artificial immune systems and fuzzy systems. The algorithm consists of two parts: a sequential covering procedure and a rule evolution procedure. Each antibody (candidate solution) corresponds to a classification rule. The classification of new examples (antigens) considers not only the fitness of a fuzzy rule based on the entire training set, but also the affinity between the rule and the new example. This affinity must be greater than a threshold in order for the fuzzy rule to be activated, and it is proposed an adaptive procedure for computing this threshold for each rule. This paper reports results for the proposed algorithm in several data sets. Results are analyzed with respect to both predictive accuracy and rule set simplicity, and are compared with C4.5rules, a very popular data mining algorithm.

Item Type: Conference or workshop item (Paper)
Uncontrolled keywords: artificial immune systems, data mining, classification
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming,
Divisions: Faculties > Sciences > School of Computing > Applied and Interdisciplinary Informatics Group
Depositing User: Mark Wheadon
Date Deposited: 24 Nov 2008 18:01 UTC
Last Modified: 14 Jan 2017 14:31 UTC
Resource URI: (The current URI for this page, for reference purposes)
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