mlsanityData to 7 Oct 2026

RuleMatrix: Visualizing and Understanding Classifiers with Rules

arXiv 1807.06228 ↗First posted 17 Jul 2018cs.LG cs.AI cs.HCcited by 62

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Abstract (arXiv)

With the growing adoption of machine learning techniques, there is a surge of research interest towards making machine learning systems more transparent and interpretable. Various visualizations have been developed to help model developers understand, diagnose, and refine machine learning models. However, a large number of potential but neglected users are the domain experts with little knowledge of machine learning but are expected to work with machine learning systems. In this paper, we present an interactive visualization technique to help users with little expertise in machine learning to understand, explore and validate predictive models. By viewing the model as a black box, we extract a standardized rule-based knowledge representation from its input-output behavior. We design RuleMatrix, a matrix-based visualization of rules to help users navigate and verify the rules and the black-box model. We evaluate the effectiveness of RuleMatrix via two use cases and a usability study.

Key facts. Cited by 62 papers in the map (data through 7 Oct 2026). First posted on arXiv 17 Jul 2018.

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Citing papers per year: 2021: 15; 2022: 10; 2023: 18; 2024: 10; 2025: 6; 2026: 3.

62 papers.

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  1. 2021Benchmarking and Survey of Explanation Methods for Black Box Modelscited by 136
  2. 2021Unbox the Black-box for the Medical Explainable AI via Multi-modal and Multi-centre Data Fusion: A Mini-Review, Two Showcases and Beyondcited by 75
  3. 2024Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Directioncited by 46
  4. 2022The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizationscited by 29
  5. 2024LLM Comparator: Visual Analytics for Side-by-Side Evaluation of Large Language Modelscited by 29
  6. 2022Aligning Eyes between Humans and Deep Neural Network through Interactive Attention Alignmentcited by 27
  7. 2021To trust or not to trust an explanation: using LEAF to evaluate local linear XAI methodscited by 26
  8. 2023Visual Analytics For Machine Learning: A Data Perspective Surveycited by 25
  9. 2023A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?cited by 24
  10. 2021A Holistic Approach to Interpretability in Financial Lending: Models, Visualizations, and Summary-Explanationscited by 16
  11. 2023Where Are We So Far? Understanding Data Storytelling Tools from the Perspective of Human-AI Collaborationcited by 13
  12. 2021VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Modelscited by 12
38 more
  1. 2021Efficient Decompositional Rule Extraction for Deep Neural Networkscited by 11
  2. 2022Explainable AI for Bioinformatics: Methods, Tools, and Applicationscited by 11
  3. 2021Towards Visual Explainable Active Learning for Zero-Shot Classificationcited by 10
  4. 2022Learning-From-Disagreement: A Model Comparison and Visual Analytics Frameworkcited by 9
  5. 2021Developing a Fidelity Evaluation Approach for Interpretable Machine Learningcited by 8
  6. 2021VisRuler: Visual Analytics for Extracting Decision Rules from Bagged and Boosted Decision Treescited by 8
  7. 2023Towards an Understanding and Explanation for Mixed-Initiative Artificial Scientific Text Detectioncited by 8
  8. 2023Leveraging Model-based Trees as Interpretable Surrogate Models for Model Distillationcited by 8
  9. 2024Deconstructing Human-AI Collaboration: Agency, Interaction, and Adaptationcited by 8
  10. 2021An Exploration And Validation of Visual Factors in Understanding Classification Rule Setscited by 5
  11. 2022Visual Exploration of Machine Learning Model Behavior with Hierarchical Surrogate Rule Setscited by 5
  12. 2022HardVis: Visual Analytics to Handle Instance Hardness Using Undersampling and Oversampling Techniquescited by 5
  13. 2023Visual Explanations with Attributions and Counterfactuals on Time Series Classificationcited by 5
  14. 2021Multivariate Data Explanation by Jumping Emerging Patterns Visualizationcited by 4
  15. 2021Non-linear Visual Knowledge Discovery with Elliptic Paired Coordinatescited by 4
  16. 2022Interpreting Black-box Machine Learning Models for High Dimensional Datasetscited by 4
  17. 2023Invisible Users: Uncovering End-Users' Requirements for Explainable AI via Explanation Forms and Goalscited by 4
  18. 2024MOUNTAINEER: Topology-Driven Visual Analytics for Comparing Local Explanationscited by 4
  19. 2022Visualization of Decision Trees based on General Line Coordinates to Support Explainable Modelscited by 3
  20. 2023Designing a Direct Feedback Loop between Humans and Convolutional Neural Networks through Local Explanationscited by 3
  21. 2023Are We Closing the Loop Yet? Gaps in the Generalizability of VIS4ML Researchcited by 3
  22. 2024Towards consistency of rule-based explainer and black box model -- fusion of rule induction and XAI-based feature importancecited by 3
  23. 2021AHMoSe: A Knowledge-Based Visual Support System for Selecting Regression Machine Learning Modelscited by 2
  24. 2022Computing Rule-Based Explanations of Machine Learning Classifiers using Knowledge Graphscited by 2
  25. 2022Transcending XAI Algorithm Boundaries through End-User-Inspired Designcited by 2
  26. 2023DeforestVis: Behavior Analysis of Machine Learning Models with Surrogate Decision Stumpscited by 2
  27. 2024DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machinecited by 2
  28. 2024Understanding the Dataset Practitioners Behind Large Language Model Developmentcited by 2
  29. 2023Interactive Decision Tree Creation and Enhancement with Complete Visualization for Explainable Modelingcited by 1
  30. 2023GeoExplainer: A Visual Analytics Framework for Spatial Modeling Contextualization and Report Generationcited by 1
  31. 2024RuleExplorer: A Scalable Matrix Visualization for Understanding Tree Ensemble Classifierscited by 1
  32. 2025Cluster-Based Random Forest Visualization and Interpretationcited by 1
  33. 2021Visualizing Rule Sets: Exploration and Validation of a Design Spacecited by 0
  34. 2021Improved prediction rule ensembling through model-based data generationcited by 0
  35. 2023Interpretable Outlier Summarizationcited by 0
  36. 2023DASS Good: Explainable Data Mining of Spatial Cohort Datacited by 0
  37. 2023Designing Explainable Predictive Machine Learning Artifacts: Methodology and Practical Demonstrationcited by 0
  38. 2023Declarative Reasoning on Explanations Using Constraint Logic Programmingcited by 0

50 most cited of 62 All 62 (JSON)