pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules

• URL: http://arxiv.org/abs/2105.13850v1
• Date: Fri, 28 May 2021 14:06:21 GMT
• Title: pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules
• Authors: Kirchhof Michael and Schmid Lena and Reining Christopher and ten Hompel Michael and Pauly Markus
• Abstract summary: We present the probabilistic rule stacking (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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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