Probabilistic multi-dimensional classification with incomplete data at the prediction time

Multi-dimensional classification (MDC) extends multi-class and multi-label learning by predicting several class variables per instance. We revisit probabilistic MDC methods with mixed features (discrete and continuous), focusing on their strengths and limits for handling incomplete data at prediction time. We present theoretical results leading to a new probabilistic approach with efficient learning and prediction algorithms that address scalability and robustness issues. Experiments demonstrate its benefits in different missingness scenarios.