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

DOI:

https://doi.org/10.1609/aaai.v32i1.12172

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.

AAAI 2018 Proceedings Cover

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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). https://doi.org/10.1609/aaai.v32i1.12172

Issue

Vol. 32 No. 1 (2018): Thirty-Second AAAI Conference on Artificial Intelligence

Section

Student Abstract Track