Scaling the Explanation of Multi-Class Bayesian Network Classifiers

Fuente: arXiv
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Main Authors: Zhang, Yaofang, Darwiche, Adnan
Format: Preprint
Published: 2026
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author Zhang, Yaofang
Darwiche, Adnan
author_facet Zhang, Yaofang
Darwiche, Adnan
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2603_14594
institution arXiv
publishDate 2026
record_format arxiv
spellingShingle Scaling the Explanation of Multi-Class Bayesian Network Classifiers
Zhang, Yaofang
Darwiche, Adnan
Artificial Intelligence
Machine Learning
Logic in Computer Science
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.
title Scaling the Explanation of Multi-Class Bayesian Network Classifiers
topic Artificial Intelligence
Machine Learning
Logic in Computer Science
url https://arxiv.org/abs/2603.14594