Name
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. |
Year | Venue | Field |
|---|---|---|
2018 | THIRTY-SECOND AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTIETH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / EIGHTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | Feature selection,Computer science,Feature ranking,Artificial intelligence,Machine learning |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| R. Chen | 1 | 2 | 1.83 |
| Xiaojun Chen | 2 | 1298 | 107.51 |
| Guowen Yuan | 3 | 26 | 3.05 |
| Wenya Sun | 4 | 8 | 0.76 |
| Wu Qingyao | 5 | 259 | 33.46 |