2021/05/28 by Michael Kirchhof, Lena Schmid, Kirchhof, Michael +7
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Computation (stat.CO) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning and Data Classification #Topic Modeling #cs.LG #stat.CO #stat.ML
paper · pdf · doi:10.48550/arxiv.2105.13850
arxiv created 2021/05/28 · openalex publication_date 2021/05/28 · arxiv updated 2021/06/08 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.