Scaling the Explanation of Multi-Class Bayesian Network Classifiers

TL;DR

This paper introduces a scalable algorithm for converting multi-class Bayesian network classifiers into logical class formulas, enhancing explanation capabilities with improved efficiency and properties.

Contribution

It presents a novel, efficient algorithm for compiling multi-class Bayesian network classifiers into class formulas in negation normal form, overcoming previous limitations.

Findings

Significant reduction in compilation time.

Supports multi-class classifiers, not just binary.

Produces OR-decomposable NNF circuits for explanations.

Abstract

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.