Quality, Frequency and Similarity Based Fuzzy Nearest Neighbor Classification

Nele Verbiest, Chris Cornelis, Richard Jensen

Allbwn ymchwil: Pennod mewn Llyfr/Adroddiad/Trafodion CynhadleddTrafodion Cynhadledd (Nid-Cyfnodolyn fathau)

4 Dyfyniadau(SciVal)
127 Wedi eu Llwytho i Lawr (Pure)

Crynodeb

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.
Iaith wreiddiolSaesneg
Teitl2013 IEEE International Conference on Fuzzy Systems (FUZZ)
CyhoeddwrIEEE Press
Tudalennau1-8
ISBN (Argraffiad)978-1-4799-0020-6
Dynodwyr Gwrthrych Digidol (DOIs)
StatwsCyhoeddwyd - 2013
DigwyddiadFuzzy Systems - Hyderabad, Hyderabad, India
Hyd: 07 Gorff 201310 Gorff 2013
Rhif y gynhadledd: 22

Cynhadledd

CynhadleddFuzzy Systems
Teitl crynoFUZZ-IEEE-2013
Gwlad/TiriogaethIndia
DinasHyderabad
Cyfnod07 Gorff 201310 Gorff 2013

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