Explainable Evidential Clustering

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.

A review of 3D human pose estimation algorithms for markerless motion capture

Human pose estimation (HPE) in 3D is an active research field that have many applications in entertainment, health and sport science, robotics. In the last five years markerless motion captures techniques have seen their average error decrease from more than 10cm to less than 2cm today. This evolution is mainly driven by the improvements in 2D pose estimation task that benefited from the use of convolutional networks. However with the multiplication of different approaches it can be difficult to identify what is more adapted to the specifics of any applications. We suggest to classify existing methods with a taxonomy based on the performance criteria of accuracy, speed and robustness. We review more than twenty methods from the last three years. Additionally we analyze the metrics, benchmarks and structure of the different pose estimation systems and propose several direction for future research. We hope to offer a good introduction to 3D markerless pose estimation as well as discussing the leading contemporary algorithms.