mlsanityData to 14 Oct 2026

Boolean Decision Rules via Column Generation

arXiv 1805.09901 ↗First posted 24 May 2018cs.AIcited by 80

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Abstract (arXiv)

This paper considers the learning of Boolean rules in either disjunctive normal form (DNF, OR-of-ANDs, equivalent to decision rule sets) or conjunctive normal form (CNF, AND-of-ORs) as an interpretable model for classification. An integer program is formulated to optimally trade classification accuracy for rule simplicity. Column generation (CG) is used to efficiently search over an exponential number of candidate clauses (conjunctions or disjunctions) without the need for heuristic rule mining. This approach also bounds the gap between the selected rule set and the best possible rule set on the training data. To handle large datasets, we propose an approximate CG algorithm using randomization. Compared to three recently proposed alternatives, the CG algorithm dominates the accuracy-simplicity trade-off in 7 out of 15 datasets. When maximized for accuracy, CG is competitive with rule learners designed for this purpose, sometimes finding significantly simpler solutions that are no less accurate.

Key facts. Cited by 80 papers in the map (data through 14 Oct 2026). First posted on arXiv 24 May 2018.

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Citing papers per year: 2021: 19; 2022: 20; 2023: 17; 2024: 14; 2025: 6; 2026: 4.

80 papers.

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  1. 2021Interpretable Machine Learning: Fundamental Principles and 10 Grand Challengescited by 367
  2. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  3. 2022Leveraging Explanations in Interactive Machine Learning: An Overviewcited by 53
  4. 2021The Who in XAI: How AI Background Shapes Perceptions of AI Explanationscited by 48
  5. 2021Fast Sparse Decision Tree Optimization via Reference Ensemblescited by 38
  6. 2021MEGEX: Data-Free Model Extraction Attack against Gradient-Based Explainable AIcited by 35
  7. 2021Learning Accurate and Interpretable Decision Rule Sets from Neural Networkscited by 25
  8. 2021A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanationscited by 16
  9. 2021Controlling Neural Networks with Rule Representationscited by 16
  10. 2022From Black Boxes to Conversations: Incorporating XAI in a Conversational Agentcited by 14
  11. 2022Learning Interpretable Decision Rule Sets: A Submodular Optimization Approachcited by 13
  12. 2021Explaining Network Intrusion Detection System Using Explainable AI Frameworkcited by 11
38 more
  1. 2021Efficient Decompositional Rule Extraction for Deep Neural Networkscited by 11
  2. 2021Interpretable Clustering via Multi-Polytope Machinescited by 11
  3. 2021Interpretable and Fair Boolean Rule Sets via Column Generationcited by 10
  4. 2024Optimization Over Trained Neural Networks: Taking a Relaxing Walkcited by 10
  5. 2023Scalable Optimal Multiway-Split Decision Trees with Constraintscited by 9
  6. 2021AI Explainability 360: Impact and Designcited by 8
  7. 2022Differentiable Rule Induction with Learned Relational Featurescited by 8
  8. 2021A Scalable Two Stage Approach to Computing Optimal Decision Setscited by 7
  9. 2023Efficient learning of large sets of locally optimal classification rulescited by 7
  10. 2022Truly Unordered Probabilistic Rule Sets for Multi-class Classificationcited by 6
  11. 2023Interpretable Regional Descriptors: Hyperbox-Based Local Explanationscited by 6
  12. 2021Zero-shot learning approach to adaptive Cybersecurity using Explainable AIcited by 5
  13. 2022Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Setscited by 5
  14. 2022User Driven Model Adjustment via Boolean Rule Explanationscited by 5
  15. 2023Finding Interpretable Class-Specific Patterns through Efficient Neural Searchcited by 5
  16. 2024Learning Cut Generating Functions for Integer Programmingcited by 5
  17. 2021Rule Generation for Classification: Scalability, Interpretability, and Fairnesscited by 4
  18. 2021Fair Decision Rules for Binary Classificationcited by 4
  19. 2021Rule Induction in Knowledge Graphs Using Linear Programmingcited by 4
  20. 2022Mixed integer linear optimization formulations for learning optimal binary classification treescited by 4
  21. 2022A Scalable, Interpretable, Verifiable & Differentiable Logic Gate Convolutional Neural Network Architecture From Truth Tablescited by 4
  22. 2023Neural-based classification rule learning for sequential datacited by 4
  23. 2024Trust Regions for Explanations via Black-Box Probabilistic Certificationcited by 4
  24. 2022FROTE: Feedback Rule-Driven Oversampling for Editing Modelscited by 3
  25. 2022Interpretable Off-Policy Learning via Hyperbox Searchcited by 3
  26. 2023CGXplain: Rule-Based Deep Neural Network Explanations Using Dual Linear Programscited by 3
  27. 2023Discovering Intrinsic Spatial-Temporal Logic Rules to Explain Human Actionscited by 3
  28. 2024Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importancecited by 3
  29. 2024Biological and Radiological Dictionary of Radiomics Features: Addressing Understandable AI Issues in Personalized Prostate Cancer; Dictionary Version PM1.0cited by 3
  30. 2025CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractionscited by 3
  31. 2022Analogies and Feature Attributions for Model Agnostic Explanation of Similarity Learnerscited by 2
  32. 2022NetRCA: An Effective Network Fault Cause Localization Algorithmcited by 2
  33. 2022Policy Optimization with Sparse Global Contrastive Explanationscited by 2
  34. 2022Boolean Decision Rules for Reinforcement Learning Policy Summarisationcited by 2
  35. 2022Interpretable Selective Learning in Credit Riskcited by 2
  36. 2023Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalitiescited by 2
  37. 2024Orthogonal Gradient Boosting for Simpler Additive Rule Ensemblescited by 2
  38. 2024Forest-ORE: Mining Optimal Rule Ensemble to interpret Random Forest modelscited by 2

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