A New Approach to Fuzzy-Rough Nearest Neighbour Classification

Richard Jensen, Chris Cornelis

Research output: Chapter in Book/Report/Conference proceedingChapter

60 Citations (SciVal)
181 Downloads (Pure)

Abstract

In this paper, we present a new fuzzy-rough nearest neighbour (FRNN) classification algorithm, 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 both FRNN-O, as well as the traditional fuzzy nearest neighbour (FNN) algorithm
Original languageEnglish
Title of host publicationRough Sets and Current Trends in Computing
Subtitle of host publicationProceedings 6th International Conference, RSCTC 2008 Akron
EditorsChien-Chung Chan, Jerzy W. Grzymala-Busse, Wojciech P. Ziarko
PublisherSpringer Nature
Pages310-319
Number of pages10
ISBN (Electronic)978-3-540-88425-5
ISBN (Print)978-3-540-88423-1
DOIs
Publication statusPublished - 2008
EventProceedings 6th International Conference on Rough Sets and Current Trends in Computing: RSCTC 2008 - , United States of America
Duration: 23 Oct 200825 Oct 2008

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceProceedings 6th International Conference on Rough Sets and Current Trends in Computing
Country/TerritoryUnited States of America
Period23 Oct 200825 Oct 2008

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