Enhancing Fuzzy Learning with Data Mining Techniques

Aleksander Satanislaw Przybylo, Sofiane Achiche, Luc Baron, Marek Balazinski

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Decision support systems using fuzzy logic generally deal with complex and large decision making problems. This can be particularly problematic in case of systems requiring a fast and accurate response. This paper proposes a technique that lets us accelerate and improve the automatic creation of the Fuzzy Knowledge Bases performed by the use of Genetic Algorithms. First a brief description of the Fuzzy Decision Support System software Fuzzy-Flou and the specificities of the Real Binary-Like Coded Genetic algorithm in terms of reproduction and mutation mechanisms are presented. Later on, an Exploratory Data Analysis strategy and clustering algorithms are discussed and, finally, the results are validated through a theoretical 3D surface.
Original languageEnglish
Title of host publicationArtificial Intelligence Methods
Volume1
Publication date2004
Pages108-110
Publication statusPublished - 2004
Externally publishedYes
EventArtificial Intelligence Methods - Gliwice, Poland
Duration: 1 Jan 2004 → …

Conference

ConferenceArtificial Intelligence Methods
CityGliwice, Poland
Period01/01/2004 → …

Cite this

Przybylo, A. S., Achiche, S., Baron, L., & Balazinski, M. (2004). Enhancing Fuzzy Learning with Data Mining Techniques. In Artificial Intelligence Methods (Vol. 1, pp. 108-110)