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Wenya Sun

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AAAI Conference 2018 Short Paper

A Stratified Feature Ranking Method for Supervised Feature Selection

Renjie Chen
Xiaojun Chen
Guowen Yuan
Wenya Sun
Qingyao Wu

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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