Learning Explainable Decision Rules via Maximum Satisfiability

Learning Explainable Decision Rules via Maximum Satisfiability

Decision trees are a popular choice for providing explainable machine learning, since they make explicit how different features contribute towards the prediction. We apply tools from constraint satisfaction to learn optimal decision trees in the form of sparse k-CNF (Conjunctive Normal Form) rules....

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Bibliographic Details
Main Authors: Henrik E. C. Cao, Riku Sarlin, Alexander Jung
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Classification
combinatorial optimization
decision tree
explainable AI
machine learning
weighted MaxSAT
Online Access:https://ieeexplore.ieee.org/document/9272729/

Internet

https://ieeexplore.ieee.org/document/9272729/

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