Feature subset selection for logistic regression via mixed integer optimization

  title={Feature subset selection for logistic regression via mixed integer optimization},
  author={Toshiki Sato and Yuichi Takano and Ryuhei Miyashiro and Akiko Yoshise},
  journal={Comp. Opt. and Appl.},
This paper concerns a method of selecting a subset of features for a logistic regression model. Information criteria, such as the Akaike information criterion and Bayesian information criterion, are employed as a goodness-offit measure. The feature subset selection problem is formulated as a mixed integer linear optimization problem, which can be solved with standard mathematical optimization software, by using a piecewise linear approximation. Computational experiments show that, in terms of… CONTINUE READING
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Feature Subset Selection for Logistic Regression via Mixed Integer Optimization

  • T. Sato, Y. Takano, R. Miyashiro, A. Yoshise
  • Computational Optimization and Applications Vol…
  • 2016

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