Stochastic correlation coefficient ensembles for variable selection

JinXing Che and YouLong Yang

Journal of Applied Statistics, 2017, vol. 44, issue 10, 1721-1742

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.

Date: 2017
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DOI: 10.1080/02664763.2016.1221913

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