Flexible categorization using formal concept analysis and Dempster-Shafer theory
This addresses the need for interpretable categorization in machine learning, though it appears incremental as it builds on existing formal methods.
The paper tackles the problem of generating explainable categorizations for sets of entities based on agent attitudes, resulting in a meta-algorithm for outlier detection and classification that provides local and global explanations.
The framework developed in the present paper provides a formal ground to generate and study explainable categorizations of sets of entities, based on the epistemic attitudes of individual agents or groups thereof. Based on this framework, we discuss a machine-leaning meta-algorithm for outlier detection and classification which provides local and global explanations of its results.