A New Approach to Fuzzy-Rough Nearest Neighbour Classification

Richard Jensen, Chris Cornelis

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

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Abstract

A new fuzzy-rough nearest neighbour (FRNN) classification algorithm is presented in this paper, as an alternative to Sarkar’s fuzzy-rough ownership function (FRNN-O) approach. By contrast to the latter, our method uses the nearest neighbours to construct lower and upper approximations of decision classes, and classifies test instances based on their membership to these approximations. In the experimental analysis, we evaluate our approach with both classical fuzzy-rough approximations (based on an implicator and a t-norm), as well as with the recently introduced vaguely quantified rough sets. Preliminary results are very good, and in general FRNN outperforms FRNN-O, as well as the traditional fuzzy nearest neighbour (FNN) algorithm.
Original languageEnglish
Title of host publicationProceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
Pages310-319
Number of pages10
Publication statusPublished - 2011

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