A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values

Han, Joon H.; Kim, Yoon K.

doi:10.1109/CVPR.1999.784711

A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values

Joon H. Han, Yoon K. Kim

CVPR 1999 pp. 2394-2399

doi:10.1109/CVPR.1999.784711 /cvpr/1999/han1999cvpr-fuzzy/

Abstract

In this paper, a new fuzzy K-nearest neighbor (K-NN) algorithm, called "Variance Weighted Fuzzy K-NN", is proposed. The main idea of this method is in giving weights to neighbors according to the standard deviation of their class membership values which reflect the value of a discriminant function. The classification results of 32 classes of complex images are given. Compared to the K-NN and fuzzy K-NN algorithms, our method shows an improved classification rate for various conditions.

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Cite

Text

Han and Kim. "A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999. doi:10.1109/CVPR.1999.784711

Markdown

[Han and Kim. "A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1999.](https://mlanthology.org/cvpr/1999/han1999cvpr-fuzzy/) doi:10.1109/CVPR.1999.784711

BibTeX

@inproceedings{han1999cvpr-fuzzy,
  title     = {{A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values}},
  author    = {Han, Joon H. and Kim, Yoon K.},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  year      = {1999},
  pages     = {2394-2399},
  doi       = {10.1109/CVPR.1999.784711},
  url       = {https://mlanthology.org/cvpr/1999/han1999cvpr-fuzzy/}
}