Three-way Learnability: A Learning Theoretic Perspective on Three-way Decision
Andrea Campagner, Davide Ciucci · Annals of Computer Science and Information Systems · 2022
In this article we study the theoretical properties of Three-way Decision (TWD) based Machine Learning, from the perspective of Computational Learning Theory, as a first attempt to bridge the gap between Machine Learning theory and Uncertainty Representation theory.Drawing on the mathematical theory of orthopairs, we provide a generalization of the PAC learning framework to the TWD setting, and we use this framework to prove a generalization of the Fundamental Theorem of Statistical Learning.We then show, by means of our main result, a connection between TWD and selective prediction.