A Symbolic Approach to Explaining Bayesian Network Classifiers.
Andy Shih,Arthur Choi,Adnan Darwiche +2 more
- pp 5103-5111
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In this article, the authors 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.Abstract:
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.read more
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Proceedings Article
On relating explanations and adversarial examples
TL;DR: It is demonstrated that explanations and adversarial examples are related by a generalized form of hitting set duality, which extends earlier work on hitting setDuality observed in model-based diagnosis and knowledge compilation.
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On The Reasons Behind Decisions.
Adnan Darwiche,Auguste Hirth +1 more
TL;DR: A theory for unveiling the reasons behind the decisions made by Boolean classifiers is presented and notions such as sufficient, necessary and complete reasons behind decisions are defined, in addition to classifier and decision bias.