A comparative study on swarm intelligence for structure learning of Bayesian networks

Junzhong Ji*, Cuicui Yang, Jiming LIU, Jinduo Liu, Baocai Yin

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

A Bayesian network (BN) is an important probabilistic model in the field of artificial intelligence and a powerful formalism used to describe uncertainty in the real world. As science and technology develop, considerable data on complex systems have been acquired by various means, which presents a significant challenge regarding how to accurately and robustly learn a network structure for a complex system. To address this challenge, many BN structure learning methods based on swarm intelligence have been developed. In this study, we perform a systematic comparison of three typical methods based on ant colony optimization, artificial bee colony algorithm, and bacterial foraging optimization. First, we analyze and summarize their main characteristics from the perspective of stochastic searching. Second, we conduct thorough experimental comparisons to examine the roles of different mechanisms in each method by means of multiaspect metrics, i.e., the K2 score, structural differences, and execution time. Next, we perform further experiments to validate the robustness of different algorithms on some benchmark data sets with noise. Finally, we present the prospects and references for researchers who are engaged in learning BN networks.

Original languageEnglish
Pages (from-to)6713-6738
Number of pages26
JournalSoft Computing
Volume21
Issue number22
DOIs
Publication statusPublished - 1 Nov 2017

Scopus Subject Areas

  • Software
  • Theoretical Computer Science
  • Geometry and Topology

User-Defined Keywords

  • Ant colony optimization
  • Artificial bee colony algorithm
  • Bacterial foraging optimization
  • Bayesian network structure learning
  • Swarm intelligence

Fingerprint

Dive into the research topics of 'A comparative study on swarm intelligence for structure learning of Bayesian networks'. Together they form a unique fingerprint.

Cite this