Advanced search
1 file | 342.91 KB

Quality, frequency and similarity based fuzzy nearest neighbor classification

Author
Organization
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.
Keywords
Classification, Fuzzy Rough Set Theory, Nearest Neighbors, Ordered Weighted Average, ROUGH SETS

Downloads

  • VerbiestFUZZIEEE2013
    • full text
    • |
    • open access
    • |
    • PDF
    • |
    • 342.91 KB

Citation

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

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.
@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},
}

Altmetric
View in Altmetric
Web of Science
Times cited: