Stochastic correlation coefficient ensembles for variable selection |
| |
Authors: | JinXing Che |
| |
Institution: | School of Mathematics and Statistics, Xidian University, Xi'an, People's Republic of China |
| |
Abstract: | In this paper, we propose a novel Max-Relevance and Min-Common-Redundancy criterion for variable selection in linear models. Considering that the ensemble approach for variable selection has been proven to be quite effective in linear regression models, we construct a variable selection ensemble (VSE) by combining the presented stochastic correlation coefficient algorithm with a stochastic stepwise algorithm. We conduct extensive experimental comparison of our algorithm and other methods using two simulation studies and four real-life data sets. The results confirm that the proposed VSE leads to promising improvement on variable selection and regression accuracy. |
| |
Keywords: | LASSO ranking stochastic correlation coefficient ensemble maximal relevance minimal redundancy |
|
|