Explainable Evidential Clustering
This work addresses the underexplored need for explainability in evidential clustering, particularly for high-stakes applications such as healthcare, though it is incremental in extending existing concepts to this specific domain.
The paper tackles the problem of explaining evidential clustering results, which is crucial for high-stakes domains like healthcare, by proposing the Iterative Evidential Mistake Minimization (IEMM) algorithm that provides interpretable decision tree explanations, achieving satisfactory explanations up to 93% of the time based on decision-maker preferences.
Unsupervised classification is a fundamental machine learning problem. Real-world data often contain imperfections, characterized by uncertainty and imprecision, which are not well handled by traditional methods. Evidential clustering, based on Dempster-Shafer theory, addresses these challenges. This paper explores the underexplored problem of explaining evidential clustering results, which is crucial for high-stakes domains such as healthcare. Our analysis shows that, in the general case, representativity is a necessary and sufficient condition for decision trees to serve as abductive explainers. Building on the concept of representativity, we generalize this idea to accommodate partial labeling through utility functions. These functions enable the representation of "tolerable" mistakes, leading to the definition of evidential mistakeness as explanation cost and the construction of explainers tailored to evidential classifiers. Finally, we propose the Iterative Evidential Mistake Minimization (IEMM) algorithm, which provides interpretable and cautious decision tree explanations for evidential clustering functions. We validate the proposed algorithm on synthetic and real-world data. Taking into account the decision-maker's preferences, we were able to provide an explanation that was satisfactory up to 93% of the time.