论文信息 - A Stratified Feature Ranking Method for Supervised Feature Selection

A Stratified Feature Ranking Method for Supervised Feature Selection

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.

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

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