Parallel Simulated Annealing with a Greedy Algorithm for Bayesian Network Structure Learning

Sangmin Lee, Seoung Bum Kim

Research output: Contribution to journalArticlepeer-review

Abstract

We present a hybrid algorithm called parallel simulated annealing with a greedy algorithm (PSAGA) to learn Bayesian network structures. This work focuses on simulated annealing and its parallelization with memoization to accelerate the search process. At each step of the local search, a hybrid search method combining simulated annealing with a greedy algorithm was adopted. The proposed PSAGA aims to achieve both the efficiency of parallel search and the effectiveness of a more exhaustive search. The Bayesian Dirichlet equivalence metric was used to determine an optimal structure for PSAGA. The proposed PSAGA was evaluated on seven well-known Bayesian network benchmarks generated at random. We first conducted experiments to evaluate the computational time performance of the proposed parallel search. We then compared PSAGA with existing variants of simulated annealing-based algorithms to evaluate the quality of the learned structure. Overall, the experimental results demonstrate that the proposed PSAGA shows better performance than the alternatives in terms of computational time and accuracy.

Original languageEnglish
Article number8642291
Pages (from-to)1157-1166
Number of pages10
JournalIEEE Transactions on Knowledge and Data Engineering
Volume32
Issue number6
DOIs
Publication statusPublished - 2020 Jun 1

Keywords

  • Bayesian networks
  • heuristic search algorithm
  • memoization
  • parallel structure learning
  • simulated annealing with a greedy algorithm
  • structure learning

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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