Fuzzy-Rough Nearest Neighbour Classification

Richard Jensen, Chris Cornelis

Research output: Contribution to journalArticlepeer-review

52 Citations (SciVal)

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
Pages (from-to)56-72
Number of pages17
JournalTransactions on Rough Sets
VolumeXIII
DOIs
Publication statusPublished - 31 Dec 2011

Fingerprint

Dive into the research topics of 'Fuzzy-Rough Nearest Neighbour Classification'. Together they form a unique fingerprint.

Cite this