Fuzzy Rough Positive Region based Nearest Neighbour Classification

Nele Verbiest, Chris Cornelis, Richard Jensen

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

19 Citations (SciVal)
127 Downloads (Pure)

Abstract

This paper proposes a classifier that uses fuzzy rough set theory to improve the Fuzzy Nearest Neighbour (FNN) classifier. We show that previous attempts to use fuzzy rough set theory to improve the FNN algorithm have some shortcomings and we overcome them by using the fuzzy positive region to measure the quality of the nearest neighbours in the FNN classifier. A preliminary experimental evaluation shows that the new approach generally improves upon existing methods.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Place of PublicationNEW YORK
PublisherIEEE Press
Pages1961-1967
Number of pages7
ISBN (Print)978-1-4673-1506-7
Publication statusPublished - 2012
EventIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI) - Brisbane
Duration: 10 Jun 201215 Jun 2012

Conference

ConferenceIEEE International Conference on Fuzzy Systems (FUZZ-IEEE)/International Joint Conference on Neural Networks (IJCNN)/IEEE Congress on Evolutionary Computation (IEEE-CEC)/IEEE World Congress on Computational Intelligence (IEEE-WCCI)
CityBrisbane
Period10 Jun 201215 Jun 2012

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