Advanced search
1 file | 252.79 KB

Fuzzy rough positive region based nearest neighbour classification

Author
Organization
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.
Keywords
SETS

Downloads

  • posnn
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 252.79 KB

Citation

Please use this url to cite or link to this publication:

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.
@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},
}

Altmetric
View in Altmetric
Web of Science
Times cited: