Consistent Joint Decision-Making with Heterogeneous Learning Models
Hossein Rajaby Faghihi Parisa Kordjamshidi
This paper introduces a novel decision-making framework that promotes
consistency among decisions made by diverse models while utilizing external
knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map
predictions from various models into globally normalized and comparable values
by incorporating information about decisions' prior probability, confidence
(uncertainty), and the models' expected accuracy. Our empirical study
demonstrates the superiority of our approach over conventional baselines on
multiple datasets.