A Method of Handling Missing Data in the Context of Learning Bayesian Network Structure

Under the background of learning Bayesian network structure, we proposed a new method based on the KNN algorithm and dynamic Gibbs sampling to fill in the missing data, which is mainly used to solve the problem of how to learn the Bayesian network structure better with missing data sets. The experiments based on Asia Network show that, this method can restore the original data very well, which will make it available to use some Bayesian network structure learning algorithm only based on complete data. This method will expand the scope and improve the effect of Bayesian networks application.

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A Bayesian Network Structure Learning Algorithm Based on the Combination of PSO and Sub-graph Decomposition

Proceedings of the 2016 International Conference on Education, Management and Computer Science ◽

10.2991/icemc-16.2016.194 ◽

2016 ◽

Author(s):

Shaorong Feng

Keyword(s):

Bayesian Network ◽

Network Structure ◽

Structure Learning ◽

Learning Algorithm ◽

Graph Decomposition ◽

Bayesian Network Structure ◽

Bayesian Network Structure Learning

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A Partial Correlation-Based Bayesian Network Structure Learning Algorithm under SEM

Advances in Knowledge Discovery and Data Mining - Lecture Notes in Computer Science ◽

10.1007/978-3-642-20847-8_6 ◽

2011 ◽

pp. 63-74

Author(s):

Jing Yang ◽

Lian Li

Keyword(s):

Bayesian Network ◽

Network Structure ◽

Partial Correlation ◽

Structure Learning ◽

Learning Algorithm ◽

Bayesian Network Structure ◽

Bayesian Network Structure Learning

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

Handbook of Research on Advancements of Swarm Intelligence Algorithms for Solving Real-World Problems - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-7998-3222-5.ch008 ◽

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.

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