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An Ensemble Learning Framework for Model Fitting and Evaluation in Inverse Linear Optimization

We develop a generalized inverse optimization framework for fitting the cost vector of a single linear optimization problem given multiple observed decisions. This setting is motivated by ensemble learning, where building consensus from base learners can yield better predictions. We unify several models in the inverse optimization literature under a single framework and derive assumption-free and exact solution methods for each one. We extend a goodness-of-fit metric previously introduced for the problem with a single observed decision to this new setting and demonstrate several important properties. Finally, we demonstrate our framework in a novel inverse optimization-driven procedure for automated radiation therapy treatment planning. Here, the inverse optimization model leverages an ensemble of dose predictions from different machine learning models to construct a consensus treatment plan that outperforms baseline methods. The consensus plan yields better trade-offs between the competing clinical criteria used for plan evaluation.

Ketersediaan

Informasi Detail

Judul Seri

INFORMS JOURNAL ON OPTIMIZATION; Vol.3 No.2 Spring 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 119 - 138

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

MODEL ESTIMATION
GOODNESS OF FIT
RADIATION THERAPY
INVERSE OPTIMIZATION
LINEAR OPTIMIZATION

Info Detail Spesifik

https://doi.org/10.1287/ijoo.2019.0045

Pernyataan Tanggungjawab

Aaron Babier, Timothy C. Y. Chan, Taewoo Lee, Rafid Mahmood, Daria Terekhov

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