A Stratified Feature Ranking Method for Supervised Feature Selection

Authors

  • Renjie Chen South China University of Technology, Guangzhou
  • Xiaojun Chen Shenzhen University, Shenzhen
  • Guowen Yuan Shenzhen University, Shenzhen
  • Wenya Sun Shenzhen University, Shenzhen
  • Qingyao Wu South China University of Technology, Guangzhou

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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Published

2018-04-29

How to Cite

Chen, R., Chen, X., Yuan, G., Sun, W., & Wu, Q. (2018). A Stratified Feature Ranking Method for Supervised Feature Selection. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/12172