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Stochastic correlation coefficient ensembles for variable selection

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  • JinXing Che
  • YouLong Yang

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.

Suggested Citation

  • JinXing Che & YouLong Yang, 2017. "Stochastic correlation coefficient ensembles for variable selection," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(10), pages 1721-1742, July.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:10:p:1721-1742
    DOI: 10.1080/02664763.2016.1221913
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    References listed on IDEAS

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