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Oral
Unbiased Objective Estimation in Predictive Optimization
Shinji Ito · Akihiro Yabe · Ryohei Fujimaki

Thu Jul 12 08:30 AM -- 08:40 AM (PDT) @ A6
in Other Models and Methods 1 »

For data-driven decision-making, one promising approach, called predictive optimization, is to solve maximization problems i n which the objective function to be maximized is estimated from data. Predictive optimization, however, suffers from the problem of a calculated optimal solution’s being evaluated too optimistically, i.e., the value of the objective function is overestimated. This paper investigates such optimistic bias and presents two methods for correcting it. The first, which is analogous to cross-validation, successfully corrects the optimistic bias but results in underestimation of the true value. Our second method employs resampling techniques to avoid both overestimation and underestimation. We show that the second method, referred to as the parameter perturbation method, achieves asymptotically unbiased estimation. Empirical results for both artificial and real-world datasets demonstrate that our proposed approach successfully corrects the optimistic bias.

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Author Information

Shinji Ito (NEC Corporation)
Akihiro Yabe (NEC Corporation)
Ryohei Fujimaki (-)

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