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pRSL: Interpretable Multi-label Stacking by Learning Probabilistic Rules [article]

Kirchhof Michael and Schmid Lena and Reining Christopher and ten Hompel Michael and Pauly Markus
2021 arXiv   pre-print
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  ...  The work on this publication was supported by Deutsche Forschungsgemeinschaft (DFG) in context of the projects "Transfer Learning for Human Activity Recognition in Logistics" (HO2463/14-2) and "Collaborative  ... 
arXiv:2105.13850v1 fatcat:aqrktgj3zrhvrgvy6m4kh56fuu