Learning Optimal Decision Sets and Lists with SAT
From MaRDI portal
Publication:5026234
DOI10.1613/jair.1.12719OpenAlexW4200315417MaRDI QIDQ5026234
Peter J. Stuckey, Pierre Le Bodic, Jinqiang Yu, Alexey Ignatiev
Publication date: 7 February 2022
Published in: Journal of Artificial Intelligence Research (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1613/jair.1.12719
Related Items (4)
Efficient Learning of Interpretable Classification Rules ⋮ SAT-based optimal classification trees for non-binary data ⋮ On optimal regression trees to detect critical intervals for multivariate functional data ⋮ Interpretable machine learning: fundamental principles and 10 grand challenges
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Learning Certifiably Optimal Rule Lists for Categorical Data
- A continuous approach to inductive inference
- Data mining. Concepts and techniques
- Learning optimal decision trees using constraint programming
- PySAT: a Python toolkit for prototyping with SAT oracles
- Learning customized and optimized lists of rules with mathematical programming
- A SAT-based approach to learn explainable decision sets
- Lower bounds on learning decision lists and trees
- Explanation in artificial intelligence: insights from the social sciences
- SAT-based encodings for optimal decision trees with explicit paths
- Branch-and-Price: Column Generation for Solving Huge Integer Programs
- Machine learning: Trends, perspectives, and prospects
- Foundations of Rule Learning
- Towards an Optimal CNF Encoding of Boolean Cardinality Constraints
- Cardinality Networks and Their Applications
- Learning Explanatory Rules from Noisy Data
- RC2: an Efficient MaxSAT Solver
- Improving Glucose for Incremental SAT Solving with Assumptions: Application to MUS Extraction
- Principles and Practice of Constraint Programming – CP 2003
This page was built for publication: Learning Optimal Decision Sets and Lists with SAT
