ML Anthology

Iterative Relief

Bruce A. Draper, Carol Kaito, José Bins
CVPRW 2003 pp. 62
doi:10.1109/CVPRW.2003.10065 /cvprw/2003/draper2003cvprw-iterative/

Abstract

Feature weighting algorithms assign weights to features according to their relevance to a particular task. Unfortunately, the best-known feature weighting algorithm, ReliefF, is biased. It decreases the relevance of some features and increases the relevance of others when irrelevant attributes are added to the data set. This paper presents an improved version of the algorithm, Iterative Relief, and shows on synthetic data that it removes the bias found in ReliefF. This paper also shows that Iterative Relief outperforms ReliefF on the task of cat and dog discrimination, using real images.

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Cite

Text

Draper et al. "Iterative Relief." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003. doi:10.1109/CVPRW.2003.10065

Markdown

[Draper et al. "Iterative Relief." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2003.](https://mlanthology.org/cvprw/2003/draper2003cvprw-iterative/) doi:10.1109/CVPRW.2003.10065

BibTeX

@inproceedings{draper2003cvprw-iterative,
  title     = {{Iterative Relief}},
  author    = {Draper, Bruce A. and Kaito, Carol and Bins, José},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2003},
  pages     = {62},
  doi       = {10.1109/CVPRW.2003.10065},
  url       = {https://mlanthology.org/cvprw/2003/draper2003cvprw-iterative/}
}