Flexible categorization using formal concept analysis and Dempster-Shafer theory

TL;DR

This paper introduces a formal framework combining formal concept analysis and Dempster-Shafer theory to generate explainable categorizations and outlier detection in machine learning, emphasizing interpretability and epistemic attitudes.

Contribution

It presents a novel formal framework for explainable categorization and a meta-algorithm for outlier detection that offers local and global explanations.

Findings

Framework enables explainable categorization based on epistemic attitudes

Meta-algorithm provides interpretable outlier detection and classification

Supports local and global explanations of results

Abstract

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.