pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules
This provides an interpretable method for multi-label classification, useful in tasks like zero-shot learning or learning from incomplete data, but it appears incremental as it builds on existing probabilistic and stacking techniques.
The paper tackles the problem of modeling class structure in multi-label classification by introducing pRSL, which uses probabilistic propositional logic rules and belief propagation to combine classifier predictions, achieving state-of-the-art performance on various benchmark datasets.
A key task in multi-label classification is modeling the structure between the involved classes. Modeling this structure by probabilistic and interpretable means enables application in a broad variety of tasks such as zero-shot learning or learning from incomplete data. In this paper, we present the probabilistic rule stacking learner (pRSL) which uses probabilistic propositional logic rules and belief propagation to combine the predictions of several underlying classifiers. We derive algorithms for exact and approximate inference and learning, and show that pRSL reaches state-of-the-art performance on various benchmark datasets. In the process, we introduce a novel multicategorical generalization of the noisy-or gate. Additionally, we report simulation results on the quality of loopy belief propagation algorithms for approximate inference in bipartite noisy-or networks.