An Algorithm for Bayesian Networks Structure Learning Based on Simulated Annealing with MDL Restriction

2008 ◽  
Author(s):  
Shuisheng Ye ◽  
Hong Cai ◽  
Rongguan Sun
Keyword(s):  
Simulated Annealing ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Networks Structure

scholarly journals Improved Local Search with Momentum for Bayesian Networks Structure Learning

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 750
Author(s):  
Xiaohan Liu ◽  
Xiaoguang Gao ◽  
Zidong Wang ◽  
Xinxin Ru
Keyword(s):  
Local Search ◽  
Complex Networks ◽  
Bayesian Networks ◽  
Structure Learning ◽  
Hybrid Methods ◽  
State Of The Art ◽  
The State ◽  
Experimental Results ◽  
Local Optimum ◽  
Networks Structure

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

scholarly journals Falcon Optimization Algorithm for Bayesian Networks Structure Learning

2021 ◽  
Vol 22 (4) ◽  
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Bayesian Networks ◽  
Bayesian Network ◽  
Optimization Algorithm ◽  
Structure Learning ◽  
Confusion Matrix ◽  
Optimal Solution ◽  
Score Function ◽  
Scientific Models ◽  
Data Sets ◽  
Networks Structure

In machine-learning, one of the useful scientific models for producing the structure of knowledge is Bayesian network, which can draw probabilistic dependency relationships between variables. The score and search is a method used for learning the structure of a Bayesian network. The authors apply the Falcon Optimization Algorithm (FOA) as a new approach to learning the structure of Bayesian networks. This paper uses the Reversing, Deleting, Moving and Inserting operations to adopt the FOA for approaching the optimal solution of Bayesian network structure. Essentially, the falcon prey search strategy is used in the FOA algorithm. The result of the proposed technique is compared with Pigeon Inspired optimization, Greedy Search, and Simulated Annealing using the BDeu score function. The authors have also examined the performances of the confusion matrix of these techniques utilizing several benchmark data sets. As shown by the evaluations, the proposed method has more reliable performance than the other algorithms including producing better scores and accuracy values.

A novel method for Bayesian networks structure learning based on Breeding Swarm algorithm

2017 ◽  
Vol 22 (9) ◽  
pp. 3049-3060 ◽  
Author(s):  
Ali Reza Khanteymoori ◽  
Mohammad-H. Olyaee ◽  
Omid Abbaszadeh ◽  
Maryam Valian
Keyword(s):  
Bayesian Networks ◽  
Structure Learning ◽  
Novel Method ◽  
Swarm Algorithm ◽  
Networks Structure

Structure Learning of Bayesian Networks Using Elephant Swarm Water Search Algorithm

2020 ◽  
Vol 11 (2) ◽  
pp. 19-30
Author(s):  
Shahab Wahhab Kareem ◽  
Mehmet Cudi Okur
Keyword(s):  
Bayesian Networks ◽  
Structure Learning ◽  
Search Algorithm ◽  
Confusion Matrix ◽  
Score Function ◽  
Analytical Models ◽  
Data Sets ◽  
Novel Approach ◽  
Networks Structure ◽  
Structure Of Knowledge

Bayesian networks are useful analytical models for designing the structure of knowledge in machine learning. Bayesian networks can represent probabilistic dependency relationships among the variables. One strategy of Bayesian Networks structure learning is the score and search technique. The authors present the Elephant Swarm Water Search Algorithm (ESWSA) as a novel approach to 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 simulated annealing and greedy search using BDe score function. The authors have also investigated the confusion matrix performances of these techniques utilizing various benchmark data sets. As presented by the results of the evaluations, the proposed algorithm has better performance than the other algorithms and produces better scores and accuracy values.

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