## Computer Science

# A direct measure for the efficacy of Bayesian network structures learned from data

## Document Type

Conference Paper

## Abstract

Current metrics for evaluating the performance of Bayesian network structure learning includes order statistics of the data likelihood of learned structures, the average data likelihood, and average convergence time. In this work, we define a new metric that directly measures a structure learning algorithm's ability to correctly model causal associations among variables in a data set. By treating membership in a Markov Blanket as a retrieval problem, we use ROC analysis to compute a structure learning algorithm's efficacy in capturing causal associations at varying strengths. Because our metric moves beyond error rate and data-likelihood with a measurement of stability, this is a better characterization of structure learning performance. Because the structure learning problem is NP-hard, practical algorithms are either heuristic or approximate. For this reason, an understanding of a structure learning algorithm's stability and boundary value conditions is necessary. We contribute to state of the art in the data-mining community with a new tool for understanding the behavior of structure learning techniques. © Springer-Verlag Berlin Heidelberg 2007.

## Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

## Publication Date

2007

## Volume

4571 LNAI

## First Page

601

## Last Page

615

## ISSN

0302-9743

## ISBN

9783540734987

## DOI

10.1007/978-3-540-73499-4_45

## Repository Citation

Holness, Gary F., "A direct measure for the efficacy of Bayesian network structures learned from data" (2007). *Computer Science*. 212.

https://commons.clarku.edu/faculty_computer_sciences/212

## APA Citation

Holness, G. F. (2007, July). A direct measure for the efficacy of Bayesian network structures learned from data. In *International Workshop on Machine Learning and Data Mining in Pattern Recognition* (pp. 601-615). Berlin, Heidelberg: Springer Berlin Heidelberg.