Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming

@article{Tule2021TrainingER,
  title={Training Experimentally Robust and Interpretable Binarized Regression Models Using Mixed-Integer Programming},
  author={Sanjana Tule and Nhi H. Le and B. Say},
  journal={2022 IEEE Symposium Series on Computational Intelligence (SSCI)},
  year={2021},
  pages={838-845}
}
  • Sanjana TuleNhi H. LeB. Say
  • Published 1 December 2021
  • Computer Science
  • 2022 IEEE Symposium Series on Computational Intelligence (SSCI)
In this paper, we explore model-based approach to training robust and interpretable binarized regression models for multiclass classification tasks using Mixed-Integer Programming (MIP). Our MIP model balances the optimization of prediction margin and model size by using a weighted objective that: minimizes the total margin of incorrectly classified training instances, maximizes the total margin of correctly classified training instances, and maximizes the overall model regularization. We… 

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