Generation of fuzzy classification rules by non-overlapping input partitioning

Ludmil Mikhailov

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The paper proposes a new method for generating fuzzy classification rules from numerical data. The main idea of the method consists in separating the input feature space into a number of non-overlapping hyperboxes, which contain input data from one classification class only, and a consequent generation of fuzzy rules and membership functions for each hyperbox. An appropriate fuzzy inference mechanism is proposed for classifying new input data into the output classification space. The proposed method formalizes the synthesis of fuzzy rule-based systems and could also be used for function approximation and design of fuzzy control systems. The method is numerically compared to some existing fuzzy classification methods using the Fisher Iris data. The comparison results show that it outperforms most of them and can successfully be used for the development of fuzzy classifiers. ©2006 IEEE.
Original languageEnglish
Title of host publicationProceedings of the 2006 International Symposium on Evolving Fuzzy Systems, EFS'06|Proc. Int. Symp. Evolving Fuzzy Syst.
PublisherIEEE
Pages365-369
Number of pages4
ISBN (Print)0780397193, 9780780397194
DOIs
Publication statusPublished - 2006
Event2006 International Symposium on Evolving Fuzzy Systems, EFS'06 - Lake District
Duration: 1 Jul 2006 → …

Conference

Conference2006 International Symposium on Evolving Fuzzy Systems, EFS'06
CityLake District
Period1/07/06 → …

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