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

2020 ◽  
Vol 32 (6) ◽  
pp. 1157-1166 ◽  
Author(s):  
Sangmin Lee ◽  
Seoung Bum Kim
Keyword(s):  
Simulated Annealing ◽  
Bayesian Network ◽  
Greedy Algorithm ◽  
Network Structure ◽  
Structure Learning ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning ◽  
Parallel Simulated Annealing

Evaluation of Bayesian Network Structure Learning Using Elephant Swarm Water Search Algorithm

2020 ◽  
pp. 139-159
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Simulated Annealing ◽  
Bayesian Network ◽  
Network Structure ◽  
Structure Learning ◽  
Search Algorithm ◽  
Confusion Matrix ◽  
Data Sets ◽  
Greedy Search ◽  
Bayesian Network Structure ◽  
Bayesian Network Structure Learning

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning which can represent probabilistic dependency relationships among the variables. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) for Bayesian network structure learning. In the algorithm; Deleting, Reversing, Inserting, and Moving are used to make the ESWSA for reaching the optimal structure solution. Mainly, water search strategy of elephants during drought periods is used in the ESWSA algorithm. The proposed method is compared with Pigeon Inspired Optimization, Simulated Annealing, Greedy Search, Hybrid Bee with Simulated Annealing, and Hybrid Bee with Greedy Search using BDeu score function as a metric for all algorithms. They investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of evaluations, the proposed algorithm achieves better performance than the other algorithms and produces better scores as well as the better values.

Sign in / Sign up

Export Citation Format

Share Document