Towards Fuzzy-Rough Rule Interpolation

Chengyuan Chen, Qiang Shen

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

1 Citation (SciVal)
22 Downloads (Pure)


Fuzzy rule interpolation is an important technique for performing inferences with sparse rule bases. Even when given observations have no overlap with the antecedent values of any rule, fuzzy rule interpolation may still derive a conclusion. Nevertheless, fuzzy rule interpolation can only handle fuzziness but not roughness. Rough set theory is a useful tool to deal with incomplete knowledge, which handles roughness but not fuzziness. Fuzzy rough sets are used to extend the original concepts in rough sets. This paper proposes a novel rule interpolation method which integrates fuzzy-rough representations with rule interpolation to deal with both fuzziness and roughness. The method follows the approach of [1],[2], using transformation-based techniques to perform interpolation, and can deal with rule interpolation in a more flexible and more robust way.
Original languageEnglish
Title of host publicationProceedings of the 11th UK Workshop on Computational Intelligence (UKCI2011)
PublisherManchester University Press
Publication statusPublished - 07 Sept 2011


Dive into the research topics of 'Towards Fuzzy-Rough Rule Interpolation'. Together they form a unique fingerprint.

Cite this