Interpolation with Just Two Nearest Neighbouring Weighted Fuzzy Rules

Type Article
Original languageEnglish
JournalIEEE Transactions on Fuzzy Systems
Early online date15 Jul 2019
DOI
Publication statusE-pub ahead of print - 15 Jul 2019
Show download statistics
View graph of relations
Citation formats

Abstract

Fuzzy rule interpolation (FRI) enables sparse fuzzy rule-based systems to derive an interpolated conclusion using neighbouring rules, when presented with an observation that matches none of the given rules. The efficacy of FRI has been further empowered by the recent development of weighted FRI techniques, particularly the one that introduces attribute weights of rule antecedents from the given rule base, removing the conventional assumption of antecedent attributes having equal weighting or significance. However, such work was carried out within the specific transformation-based FRI mechanism. This short paper reports the results of generalising it through enhancing two alternative representative FRI methods. The resultant weighted FRI algorithms facilitate the individual attribute weights to be integrated throughout the corresponding procedures of the conventional unweighted methods. With systematical comparative evaluations over benchmark classification problems, it is empirically demonstrated that these algorithms work effectively and efficiently using just two nearest neighbouring rules

Keywords

  • fuzzy interpolative reasoning, weighted rule interpolation, attribute weights, nearest neighbouring rules