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Fuzzy rough positive region based nearest neighbour classification

Nele Verbiest, Chris Cornelis UGent and Richard Jensen (2012) IEEE International Conference on Fuzzy Systems. p.1961-1967
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.
Please use this url to cite or link to this publication:
author
organization
year
type
conference (proceedingsPaper)
publication status
published
subject
keyword
SETS
in
IEEE International Conference on Fuzzy Systems
issue title
2012 IEEE International conference on fuzzy systems (FUZZ-IEEE 2012)
pages
1961 - 1967
publisher
IEEE
place of publication
New York, NY, USA
conference name
2012 IEEE International conference on Fuzzy Systems (FUZZ-IEEE 2012)
conference location
Brisbane, Australia
conference start
2012-06-10
conference end
2012-06-15
Web of Science type
Proceedings Paper
Web of Science id
000309188200273
ISSN
1098-7584
ISBN
9781467315067
DOI
10.1109/FUZZ-IEEE.2012.6251337
language
English
UGent publication?
yes
classification
P1
copyright statement
I have transferred the copyright for this publication to the publisher
id
4123233
handle
http://hdl.handle.net/1854/LU-4123233
date created
2013-08-28 12:54:43
date last changed
2018-05-17 14:42:44
@inproceedings{4123233,
  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.},
  author       = {Verbiest, Nele and Cornelis, Chris and Jensen, Richard},
  booktitle    = {IEEE International Conference on Fuzzy Systems},
  isbn         = {9781467315067},
  issn         = {1098-7584},
  keyword      = {SETS},
  language     = {eng},
  location     = {Brisbane, Australia},
  pages        = {1961--1967},
  publisher    = {IEEE},
  title        = {Fuzzy rough positive region based nearest neighbour classification},
  url          = {http://dx.doi.org/10.1109/FUZZ-IEEE.2012.6251337},
  year         = {2012},
}

Chicago
Verbiest, Nele, Chris Cornelis, and Richard Jensen. 2012. “Fuzzy Rough Positive Region Based Nearest Neighbour Classification.” In IEEE International Conference on Fuzzy Systems, 1961–1967. New York, NY, USA: IEEE.
APA
Verbiest, N., Cornelis, C., & Jensen, R. (2012). Fuzzy rough positive region based nearest neighbour classification. IEEE International Conference on Fuzzy Systems (pp. 1961–1967). Presented at the 2012 IEEE International conference on Fuzzy Systems (FUZZ-IEEE 2012), New York, NY, USA: IEEE.
Vancouver
1.
Verbiest N, Cornelis C, Jensen R. Fuzzy rough positive region based nearest neighbour classification. IEEE International Conference on Fuzzy Systems. New York, NY, USA: IEEE; 2012. p. 1961–7.
MLA
Verbiest, Nele, Chris Cornelis, and Richard Jensen. “Fuzzy Rough Positive Region Based Nearest Neighbour Classification.” IEEE International Conference on Fuzzy Systems. New York, NY, USA: IEEE, 2012. 1961–1967. Print.