Structure Learning of Bayesian Networks Based On the LARS-MMPC Ordering Search Method

He, Chuchao; Gao, Xiaoguang

doi:10.23919/chicc.2018.8483049

Cited by 5 publications

(5 citation statements)

References 14 publications

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“…Deterministic approaches, on the other hand, are vulnerable to finding local optima, are said to be weak at generalizing, and may be wide in their quest space (Shahab Wahhab Kareem, Mehmet Cudi Okur, 2019). When a local maximum has been reached, an easy solution to the problem is to use a stochastic approach this paper introduces complex Bayesian networks using particle swarm optimization (PSO) (He, C.-C., & Gao, X.-G, 2018, July 25-27). The authors choose that as a result of the job.…”

Section: Analysis Of Findingsmentioning

confidence: 99%

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

2022

QZJ

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Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. "Deleting, reversing, moving, and inserting" is an approach to finding the best answer to the proposition of the problem in the algorithm. In the Enhanced Surface Water Searching Technique, most of the hunting for water is done by elephants during dry seasons. Pigeon Optimization, Simulated Annealing, Greedy Search, and the BDeu metrics

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Section: Analysis Of Findingsmentioning

confidence: 99%

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

2022

QZJ

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“…An additional difficulty with missing data is that it operates as a closed form with comprehensive data, though not limited to that. What's needed for learning a database structure from incomplete data is entirely is much more effort than what's required for a well-structured database (He et al 2018). from 1998 onward, the use of DBN (Structural EM), can be seen in latent variable models.…”

Section: Table1 Findings From Related Previous Researchmentioning

confidence: 99%

“…from 1998 onward, the use of DBN (Structural EM), can be seen in latent variable models. Deterministic approaches, on the other hand, are vulnerable to finding local optima, are said to be weak at generalizing, may be wide in their quest space [33]. When a local maximum has been reached, an easy solution to the problem is to use a stochastic approach this paper introduces complex Bayesian networks using particle swarm optimization (PSO) [34].…”

Section: Table1 Findings From Related Previous Researchmentioning

confidence: 99%

Structure learning of bayesian network using swarm intelligent algorithm: a review

Kareem,

Askar,

Ahmed

2022

Bull.Socinf.The.App

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Machines using Bayesian networks can be used to construct the framework of information in artificial intelligence that connects the variables in a probabilistic way. “Deleting, reversing, moving, and inserting” is an approach to finding the best answer to the proposition of problem in the algorithm. In the Enhanced Surface Water Searching Technique, mostly, the hunt for water is done by elephants during dry seasons, It is Pigeon Optimization, Simulated Annealing, Greedy search, and the BDeu metrics being reviewed in combination to evaluate all these strategies being used in order to solve this problem. They subjected different data sets to the uncertainty matrix in an investigation to find out which of these approaches performed best. According to evaluation data, the algorithm shows stronger results and delivers better points. Additionally, this article also represents the structure learning processes for Bayesian Network as well.

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“…The information-theoretic score is implemented in techniques such as the Akaike information criterion (AIC), log-likelihood (LL), minimum description length (MDL), Bayesian information criterion (BIC), mutual information test (MIT), and normalized minimum likelihood (NML) [3]. There are various techniques of a research strategy that are intended to improve the problem of structural learning; these include particle swarm intelligence [4], the ant colony optimization algorithm [27], bee colony [13], the hybrid algorithm ( [11,15,21]), the simulated annealing algorithm [26], bacterial foraging optimization [33], genetic algorithms [19], the gene-pool optimal mixing evolutionary algorithm (GOMEA) [24], the breeding swarm algorithm [18], the binary encoding water cycle [32], pigeon-inspired optimization [16], tightening bounds [6], A* search algorithms [34], scatter search documents [5], the cuckoo optimization algorithm [1], quasi-determinism screening [25], and the minimum spanning tree algorithm [28]. Another additional metaheuristic technique that can be applied to learn the structure of Bayesian networks is falcon optimization.…”

Section: Introductionmentioning

confidence: 99%

Falcon Optimization Algorithm for Bayesian Networks Structure Learning

Kareem

Okur

2021

csci

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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.

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Structure Learning of Bayesian Networks Based On the LARS-MMPC Ordering Search Method

Cited by 5 publications

References 14 publications

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

Structure Learning of Bayesian Network Using Swarm Intelligent Algorithm A Review

Structure learning of bayesian network using swarm intelligent algorithm: a review

Falcon Optimization Algorithm for Bayesian Networks Structure Learning

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