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<p>In this paper, we show how mixed-integer conic programming can be used to directly optimize information criteria such as AIC and BIC in order to automate the model selection process for a collection of GLMs. Moreover, we propose to enhance the optimization problem with a novel linear constraint that limits pairwise correlation between the selected features and is well suited to tackle pairwise multicollinearity. Through a simulation study, we show that the proposed approach achieves high accuracy in selecting the active coefficients and outperforms naive enumeration methods aimed at subset selection.</p>

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Automated subset selection via information criteria optimization in generalized linear models

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月15日

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  • Benjamin Schwendinger,
  • Florian Schwendinger,
  • Laura Vana-Gür

摘要

In this paper, we show how mixed-integer conic programming can be used to directly optimize information criteria such as AIC and BIC in order to automate the model selection process for a collection of GLMs. Moreover, we propose to enhance the optimization problem with a novel linear constraint that limits pairwise correlation between the selected features and is well suited to tackle pairwise multicollinearity. Through a simulation study, we show that the proposed approach achieves high accuracy in selecting the active coefficients and outperforms naive enumeration methods aimed at subset selection.