This paper proposes a biobjective formulation that balances prediction accuracy and cost minimization, termed decision-driven regularization, which is shown to be numerically superior to other benchmarks, such as ordinary least squares, random forest, XGBoost, SPO+, perturbation gradient, and learning and rank, in the...
G. Loke, Qin-Shen Tang, Yangge Xiao et al.· INFORMS journal on computing· 1 citation
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