A Stratified Feature Ranking Method for Supervised Feature Selection

Renjie Chen, Xiaojun Chen, Guowen Yuan, Wenya Sun, Qingyao Wu

AAAI 2018 pp. 8059-8060

doi:10.1609/AAAI.V32I1.12172 /aaai/2018/chen2018aaai-stratified/

Abstract

Most feature selection methods usually select the highest rank features which may be highly correlated with each other. In this paper, we propose a Stratified Feature Ranking (SFR) method for supervised feature selection. In the new method, a Subspace Feature Clustering (SFC) is proposed to identify feature clusters, and a stratified feature ranking method is proposed to rank the features such that the high rank features are lowly correlated. Experimental results show the superiority of SFR.

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Cite

Text

Chen et al. "A Stratified Feature Ranking Method for Supervised Feature Selection." AAAI Conference on Artificial Intelligence, 2018. doi:10.1609/AAAI.V32I1.12172

Markdown

[Chen et al. "A Stratified Feature Ranking Method for Supervised Feature Selection." AAAI Conference on Artificial Intelligence, 2018.](https://mlanthology.org/aaai/2018/chen2018aaai-stratified/) doi:10.1609/AAAI.V32I1.12172

BibTeX

@inproceedings{chen2018aaai-stratified,
  title     = {{A Stratified Feature Ranking Method for Supervised Feature Selection}},
  author    = {Chen, Renjie and Chen, Xiaojun and Yuan, Guowen and Sun, Wenya and Wu, Qingyao},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2018},
  pages     = {8059-8060},
  doi       = {10.1609/AAAI.V32I1.12172},
  url       = {https://mlanthology.org/aaai/2018/chen2018aaai-stratified/}
}