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

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: m...

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Veröffentlicht in:	arXiv.org 2022-03
Hauptverfasser:	Tule, Sanjana, Nhi Ha Lan Le, Buser Say
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Boolean algebra Classification Datasets Experiments Integer programming Linear programming Mixed integer Optimization Regression models Regularization Robustness (mathematics) Training
Online-Zugang:	Volltext
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