Consistent Joint Decision-Making with Heterogeneous Learning Models
This addresses the challenge of harmonizing outputs from heterogeneous models, which is incremental as it builds on existing ILP methods.
The paper tackles the problem of inconsistent decisions from diverse machine learning models by introducing a framework that uses Integer Linear Programming to normalize predictions using prior probabilities, confidence, and expected accuracy. The approach shows superiority over conventional baselines on multiple datasets.
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.