Guiding fuzzy rule interpolation with information gains

Fangyi Li, Changjing Shang, Ying Li, Qiang Shen

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

2 Scopus citations

Abstract

Fuzzy rule interpolation enables fuzzy systems to perform inference with a sparse rule base. However, common approaches to fuzzy rule interpolation assume that rule antecedents are of equal significance while searching for rules to implement interpolation. As such, inaccurate or incorrect interpolated results may be produced. To help minimise the adverse impact of the equal significance assumption, this paper presents a novel approach for rule interpolation where information gain is utilised to evaluate the relative significance of rule antecedents in a given rule base. The approach is enabled by the introduction of an innovative reverse engineering technique that artificially creates training data from a given sparse rule base. The resulting method facilitates informed choice of most appropriate rules to compute interpolation. The work is implemented for scale and move transformation-based fuzzy rule interpolation, but the underlying idea can be extended to other rule interpolation methods. Comparative experimental evaluation demonstrates the efficacy of the proposed approach.

Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems - Contributions Presented at the 16th UK Workshop on Computational Intelligence, 2016
EditorsAlexander Gegov, Chrisina Jayne, Qiang Shen, Plamen Angelov
PublisherSpringer Verlag
Pages165-183
Number of pages19
ISBN (Print)9783319465616
DOIs
StatePublished - 2017
Event16th UK Workshop on Computational Intelligence, UKCI 2016 - Lancaster, United Kingdom
Duration: 7 Sep 20169 Sep 2016

Publication series

NameAdvances in Intelligent Systems and Computing
Volume513
ISSN (Print)2194-5357

Conference

Conference16th UK Workshop on Computational Intelligence, UKCI 2016
Country/TerritoryUnited Kingdom
CityLancaster
Period7/09/169/09/16

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