Quality, Frequency and Similarity Based Fuzzy Nearest Neighbor Classification

Nele Verbiest, Chris Cornelis, Richard Jensen

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

4 Citations (SciVal)
118 Downloads (Pure)


This paper proposes an approach based on fuzzy rough set theory to improve nearest neighbor based classification. Six measures are introduced to evaluate the quality of the nearest neighbors. This quality is combined with the frequency
at which classes occur among the nearest neighbors and the similarity w.r.t. the nearest neighbor, to decide which class to pick among the neighbor’s classes. The importance of each aspect is weighted using optimized weights. An experimental study shows that our method, Quality, Frequency and Similarity based Fuzzy
Nearest Neighbor (QFSNN), outperforms state-of-the-art nearest neighbor classifiers.
Original languageEnglish
Title of host publication2013 IEEE International Conference on Fuzzy Systems (FUZZ)
PublisherIEEE Press
ISBN (Print)978-1-4799-0020-6
Publication statusPublished - 2013
EventFuzzy Systems - Hyderabad, Hyderabad, India
Duration: 07 Jul 201310 Jul 2013
Conference number: 22


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2013
Period07 Jul 201310 Jul 2013


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