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A Symbolic Approach to Explaining Bayesian Network Classifiers
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We propose an approach for explaining Bayesian network classifiers, which is based on compiling such classifiers into decision functions that have a tractable and symbolic form. We introduce two types of explanations for why a classifier may have classified an instance positively or negatively and suggest algorithms for computing these explanations. The first type of explanation identifies a minimal set of the currently active features that is responsible for the current classification, while the second type of explanation identifies a minimal set of features whose current state (active or not) is sufficient for the classification. We consider in particular the compilation of Naive and Latent-Tree Bayesian network classifiers into Ordered Decision Diagrams (ODDs), providing a context for evaluating our proposal using case studies and experiments based on classifiers from the literature.
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Cited by 4 Pith papers
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ViTaX certifies targeted semifactual robustness: a minimal feature subset can be perturbed by ε without flipping a neural network from class y to a user-specified high-risk class t.
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Explain Yourself, Briefly! Self-Explaining Neural Networks with Concise Sufficient Reasons
SST trains models to produce concise sufficient reasons as an extra output, yielding faster and often smaller explanations than post-hoc methods like Anchors and SIS.
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