Finding Consensus Bayesian Network Structures

TL;DR

This paper addresses the challenge of constructing a consensus Bayesian network structure from multiple sources, proves the problem's NP-hardness, and corrects existing heuristics for deriving minimal independence maps.

Contribution

It demonstrates the NP-hardness of finding a consensus BN structure and provides corrected algorithms for minimal independence map derivation.

Findings

Methods A and B are incorrect as previously claimed.

Proposed corrections improve the reliability of the heuristics.

Consensus BN structures may not be unique, complicating their computation.

Abstract

Suppose that multiple experts (or learning algorithms) provide us with alternative Bayesian network (BN) structures over a domain, and that we are interested in combining them into a single consensus BN structure. Specifically, we are interested in that the consensus BN structure only represents independences all the given BN structures agree upon and that it has as few parameters associated as possible. In this paper, we prove that there may exist several non-equivalent consensus BN structures and that finding one of them is NP-hard. Thus, we decide to resort to heuristics to find an approximated consensus BN structure. In this paper, we consider the heuristic proposed in \citep{MatzkevichandAbramson1992,MatzkevichandAbramson1993a,MatzkevichandAbramson1993b}. This heuristic builds upon two algorithms, called Methods A and B, for efficiently deriving the minimal directed independence map of a BN structure relative to a given node ordering. Methods A and B are claimed to be correct although no proof is provided (a proof is just sketched). In this paper, we show that Methods A and B are not correct and propose a correction of them.