Expert Graphs: Synthesizing New Expertise via Collaboration
This work addresses consistency in expert collaboration for classification, but it appears incremental as it builds on known mathematical structures like the linear ordering polytope.
The paper tackles the problem of synthesizing consistent expert opinions in classification tasks with overlapping expertise, introducing the 'expert graphs' framework to analyze and predict missing opinions, and shows equivalence to the linear ordering polytope with partial sufficiency results.
Consider multiple experts with overlapping expertise working on a classification problem under uncertain input. What constitutes a consistent set of opinions? How can we predict the opinions of experts on missing sub-domains? In this paper, we define a framework of to analyze this problem, termed "expert graphs." In an expert graph, vertices represent classes and edges represent binary opinions on the topics of their vertices. We derive necessary conditions for expert graph validity and use them to create "synthetic experts" which describe opinions consistent with the observed opinions of other experts. We show this framework to be equivalent to the well-studied linear ordering polytope. We show our conditions are not sufficient for describing all expert graphs on cliques, but are sufficient for cycles.