pRSL: Interpretable multi-label stacking by learning probabilistic rules

Michael Kirchhof, Lena Schmid, Christopher Reining, Michael ten Hompel, Markus Pauly
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, PMLR 161:461-470, 2021.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v161-kirchhof21a, title = {pRSL: Interpretable multi-label stacking by learning probabilistic rules}, author = {Kirchhof, Michael and Schmid, Lena and Reining, Christopher and ten Hompel, Michael and Pauly, Markus}, booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence}, pages = {461--470}, year = {2021}, editor = {de Campos, Cassio and Maathuis, Marloes H.}, volume = {161}, series = {Proceedings of Machine Learning Research}, month = {27--30 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v161/kirchhof21a/kirchhof21a.pdf}, url = {https://proceedings.mlr.press/v161/kirchhof21a.html}, 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.} }
Endnote
%0 Conference Paper %T pRSL: Interpretable multi-label stacking by learning probabilistic rules %A Michael Kirchhof %A Lena Schmid %A Christopher Reining %A Michael ten Hompel %A Markus Pauly %B Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2021 %E Cassio de Campos %E Marloes H. Maathuis %F pmlr-v161-kirchhof21a %I PMLR %P 461--470 %U https://proceedings.mlr.press/v161/kirchhof21a.html %V 161 %X 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.
APA
Kirchhof, M., Schmid, L., Reining, C., ten Hompel, M. & Pauly, M.. (2021). pRSL: Interpretable multi-label stacking by learning probabilistic rules. Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research 161:461-470 Available from https://proceedings.mlr.press/v161/kirchhof21a.html.

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