Towards Dynamic Fuzzy Interpolation Based on Rule Assessment

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)

2 Citations (SciVal)

Abstract

Fuzzy Rule Interpolation (FRI) provides a useful mechanism to derive reasonable approximate inference outcomes when presented with a sparse rule base. However, conventional FRI techniques are exploited to generate fuzzy conclusions for observations that have no matching rules in a static rule base. Any interpolated rules created during the interpolation process are abandoned once the interpolated results are obtained, despite they may contain valuable, and general or generalisable, information about the domain problem. This paper proposes an approach that helps improve the sparse rule base dynamically, through measuring and subsequently adding certain interpolated rules that are deemed of high value into the sparse rule base. In particular, the value of an interpolated rule is assessed via its similarities with those given in the sparse rule base, in an effort to determine whether it is to be incorporated in the existing rule base. Experimental results on benchmark datasets illustrate that by the use of a dynamically enriched rule base the popular transformation-based FRI algorithm is able to produce strengthened inference outcomes over those that it generates while using just the original static rule base.

Original languageEnglish
Title of host publication2022 IEEE International Conference on Fuzzy Systems, FUZZ 2022 - Proceedings
PublisherIEEE Press
ISBN (Electronic)9781665467100
DOIs
Publication statusPublished - 2022
EventFuzzy Systems - Padua, Italy
Duration: 18 Jul 202223 Jul 2022
Conference number: 31

Publication series

NameIEEE International Conference on Fuzzy Systems
Volume2022-July
ISSN (Print)1098-7584

Conference

ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2022
Country/TerritoryItaly
CityPadua
Period18 Jul 202223 Jul 2022

Keywords

  • dynamic fuzzy rule interpolation
  • Fuzzy rule interpolation
  • rule assessment
  • sparse rule base

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