Ghent University Academic Bibliography

Advanced

Quality, frequency and similarity based fuzzy nearest neighbor classification

Nele Verbiest, Chris Cornelis UGent and Richard Jensen (2013) IEEE International Conference on Fuzzy Systems.
abstract
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
Classification, Fuzzy Rough Set Theory, Nearest Neighbors, Ordered Weighted Average, ROUGH SETS
in
IEEE International Conference on Fuzzy Systems
issue title
2013 IEEE International conference on fuzzy systems (FUZZ-IEEE 2013)
pages
8 pages
publisher
IEEE
place of publication
New York, NY, USA
conference name
2013 IEEE International conference on Fuzzy Systems (FUZZ-IEEE 2013)
conference location
Hyderabad, India
conference start
2013-07-07
conference end
2013-07-10
Web of Science type
Proceedings Paper
Web of Science id
000335342800042
ISSN
1098-7584
ISBN
9781479900206
DOI
10.1109/FUZZ-IEEE.2013.6622340
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
4123273
handle
http://hdl.handle.net/1854/LU-4123273
date created
2013-08-28 13:46:11
date last changed
2018-05-17 14:43:22
@inproceedings{4123273,
  abstract     = {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.},
  author       = {Verbiest, Nele and Cornelis, Chris and Jensen, Richard},
  booktitle    = {IEEE International Conference on Fuzzy Systems},
  isbn         = {9781479900206},
  issn         = {1098-7584},
  keyword      = {Classification,Fuzzy Rough Set Theory,Nearest Neighbors,Ordered Weighted Average,ROUGH SETS},
  language     = {eng},
  location     = {Hyderabad, India},
  pages        = {8},
  publisher    = {IEEE},
  title        = {Quality, frequency and similarity based fuzzy nearest neighbor classification},
  url          = {http://dx.doi.org/10.1109/FUZZ-IEEE.2013.6622340},
  year         = {2013},
}

Chicago
Verbiest, Nele, Chris Cornelis, and Richard Jensen. 2013. “Quality, Frequency and Similarity Based Fuzzy Nearest Neighbor Classification.” In IEEE International Conference on Fuzzy Systems. New York, NY, USA: IEEE.
APA
Verbiest, N., Cornelis, C., & Jensen, R. (2013). Quality, frequency and similarity based fuzzy nearest neighbor classification. IEEE International Conference on Fuzzy Systems. Presented at the 2013 IEEE International conference on Fuzzy Systems (FUZZ-IEEE 2013), New York, NY, USA: IEEE.
Vancouver
1.
Verbiest N, Cornelis C, Jensen R. Quality, frequency and similarity based fuzzy nearest neighbor classification. IEEE International Conference on Fuzzy Systems. New York, NY, USA: IEEE; 2013.
MLA
Verbiest, Nele, Chris Cornelis, and Richard Jensen. “Quality, Frequency and Similarity Based Fuzzy Nearest Neighbor Classification.” IEEE International Conference on Fuzzy Systems. New York, NY, USA: IEEE, 2013. Print.