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HDSI: High dimensional selection with interactions algorithm on feature selection and testing

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  • Rahi Jain
  • Wei Xu

Abstract

Feature selection on high dimensional data along with the interaction effects is a critical challenge for classical statistical learning techniques. Existing feature selection algorithms such as random LASSO leverages LASSO capability to handle high dimensional data. However, the technique has two main limitations, namely the inability to consider interaction terms and the lack of a statistical test for determining the significance of selected features. This study proposes a High Dimensional Selection with Interactions (HDSI) algorithm, a new feature selection method, which can handle high-dimensional data, incorporate interaction terms, provide the statistical inferences of selected features and leverage the capability of existing classical statistical techniques. The method allows the application of any statistical technique like LASSO and subset selection on multiple bootstrapped samples; each contains randomly selected features. Each bootstrap data incorporates interaction terms for the randomly sampled features. The selected features from each model are pooled and their statistical significance is determined. The selected statistically significant features are used as the final output of the approach, whose final coefficients are estimated using appropriate statistical techniques. The performance of HDSI is evaluated using both simulated data and real studies. In general, HDSI outperforms the commonly used algorithms such as LASSO, subset selection, adaptive LASSO, random LASSO and group LASSO.

Suggested Citation

  • Rahi Jain & Wei Xu, 2021. "HDSI: High dimensional selection with interactions algorithm on feature selection and testing," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-17, February.
  • Handle: RePEc:plo:pone00:0246159
    DOI: 10.1371/journal.pone.0246159
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    References listed on IDEAS

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    1. Peyman Tavallali & Marianne Razavi & Sean Brady, 2017. "A non-linear data mining parameter selection algorithm for continuous variables," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-26, November.
    2. Loann David Denis Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," Econometrics, MDPI, vol. 6(4), pages 1-27, November.
    3. Loann D. Desboulets, 2018. "A Review on Variable Selection in Regression Analysis," AMSE Working Papers 1852, Aix-Marseille School of Economics, France.
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    Cited by:

    1. Manisha Sanjay Sirsat & Paula Rodrigues Oblessuc & Ricardo S. Ramiro, 2022. "Genomic Prediction of Wheat Grain Yield Using Machine Learning," Agriculture, MDPI, vol. 12(9), pages 1-12, September.

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