mlsanityData to 14 Oct 2026

Scaling the Explanation of Multi-Class Bayesian Network Classifiers

arXiv 2603.14594 ↗First posted 15 Mar 2026cs.AI cs.LG cs.LOcited by 0

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Abstract (arXiv)

We propose a new algorithm for compiling Bayesian network classifier (BNC) into class formulas. Class formulas are logical formulas that represent a classifier's input-output behavior, and are crucial in the recent line of work that uses logical reasoning to explain the decisions made by classifiers. Compared to prior work on compiling class formulas of BNCs, our proposed algorithm is not restricted to binary classifiers, shows significant improvement in compilation time, and outputs class formulas as negation normal form (NNF) circuits that are OR-decomposable, which is an important property when computing explanations of classifiers.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 12 of its 28 references that are in the map. First posted on arXiv 15 Mar 2026.

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References · 12

  1. 2016"Why Should I Trust You?": Explaining the Predictions of Any Classifiercited by 5,070
  2. 2018Abduction-Based Explanations for Machine Learning Modelscited by 160
  3. 2018A Symbolic Approach to Explaining Bayesian Network Classifierscited by 131
  4. 2020On The Reasons Behind Decisionscited by 100
  5. 2020Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delaycited by 57
  6. 2020On Tractable Representations of Binary Neural Networkscited by 34
  7. 2019On Validating, Repairing and Refining Heuristic ML Explanationscited by 32
  8. 2022On the Computation of Necessary and Sufficient Explanationscited by 23
  9. 2023Logic for Explainable AIcited by 23
  10. 2021On Quantifying Literals in Boolean Logic and Its Applications to Explainable AIcited by 16
  11. 2023A New Class of Explanations for Classifiers with Non-Binary Featurescited by 8
  12. 2026Circuit Representations of Random Forests with Applications to XAIcited by 2

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