Interpolation with Just Two Nearest Neighboring Weighted Fuzzy Rules

Fangyi Li, Changjing Shang, Ying Li, Jing Yang, Qiang Shen

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)
227 Downloads (Pure)


Fuzzy rule interpolation (FRI) enables sparse fuzzy rule-based systems to derive an interpolated conclusion using neighboring 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 generalizing 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 neighboring rules.

Original languageEnglish
Article number8762115
Pages (from-to)2255 - 2262
Number of pages8
JournalIEEE Transactions on Fuzzy Systems
Issue number9
Early online date15 Jul 2019
Publication statusPublished - 01 Sept 2020


  • Attribute weights
  • fuzzy interpolative reasoning
  • nearest neighboring rules
  • weighted rule interpolation


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