Supervised Feature Selection via Dependence Estimation

Le Song, Alexander J. Smola, Arthur Gretton, Karsten M. Borgwardt, Justin Bedo

ICML 2007 pp. 823-830

doi:10.1145/1273496.1273600 /icml/2007/song2007icml-supervised/

Abstract

We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The key idea is that good features should maximise such dependence. Feature selection for various supervised learning problems (including classification and regression) is unified under this framework, and the solutions can be approximated using a backward-elimination algorithm. We demonstrate the usefulness of our method on both artificial and real world datasets.

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Text

Song et al. "Supervised Feature Selection via Dependence Estimation." International Conference on Machine Learning, 2007. doi:10.1145/1273496.1273600

Markdown

[Song et al. "Supervised Feature Selection via Dependence Estimation." International Conference on Machine Learning, 2007.](https://mlanthology.org/icml/2007/song2007icml-supervised/) doi:10.1145/1273496.1273600

BibTeX

@inproceedings{song2007icml-supervised,
  title     = {{Supervised Feature Selection via Dependence Estimation}},
  author    = {Song, Le and Smola, Alexander J. and Gretton, Arthur and Borgwardt, Karsten M. and Bedo, Justin},
  booktitle = {International Conference on Machine Learning},
  year      = {2007},
  pages     = {823-830},
  doi       = {10.1145/1273496.1273600},
  url       = {https://mlanthology.org/icml/2007/song2007icml-supervised/}
}