A Symbolic Approach to Explaining Bayesian Network Classifiers
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.
Cite
@article{arxiv.1805.03364,
title = {A Symbolic Approach to Explaining Bayesian Network Classifiers},
author = {Andy Shih and Arthur Choi and Adnan Darwiche},
journal= {arXiv preprint arXiv:1805.03364},
year = {2018}
}