Three-way decision and conformal prediction: Isomorphisms, differences and theoretical properties of cautious learning approaches

The aim of this article is to study the relationship between two popular Cautious Learning approaches, namely: Three-way decision (TWD) and conformal prediction (CP). Based on the novel proposal of a technique to transform three-way decision classifiers into conformal predictors, and vice versa, we provide conditions for the equivalence between TWD and CP. These theoretical results provide error-bound guarantees for TWD, together with a for -mal construction to define cost-sensitive cautious classifiers based on CP. The proposed techniques are then applied and evaluated on a collection of benchmark and real-world datasets. The results of the experiments show that the proposed techniques can be used to obtain cautious learning classifiers that are competitive with, and often out-perform, state-of-the-art approaches. Further, through a qualitative medical case study we discuss the usefulness of cautious learning in the development of robust Machine Learning. (c) 2021 Elsevier Inc. All rights reserved.

Automated summary

This article explores the relationship between Three-way decision (TWD) and conformal prediction (CP), proposing techniques to transform them into each other. Experimental results demonstrate that these techniques can be used to obtain competitive cautious learning classifiers.