A fixed structure learning automata‐based optimization algorithm for structure learning of Bayesian networks

2021 ◽  
Author(s):  
Kayvan Asghari ◽  
Mohammad Masdari ◽  
Farhad Soleimanian Gharehchopogh ◽  
Rahim Saneifard
Keyword(s):  
Bayesian Networks ◽  
Optimization Algorithm ◽  
Structure Learning ◽  
Learning Automata ◽  
Fixed Structure

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.

Bayesian Networks for Image Understanding

2007 ◽  
pp. 128-150
Author(s):  
Andreas Savaki ◽  
Jiebo Luo ◽  
Michael Kane
Keyword(s):  
Bayesian Networks ◽  
Object Detection ◽  
Bayesian Network ◽  
Structure Learning ◽  
Image Understanding ◽  
Semantic Features ◽  
Scene Classification ◽  
Low Level ◽  
Object Parts ◽  
Scene Content

Image understanding deals with extracting and interpreting scene content for use in various applications. In this chapter, we illustrate that Bayesian networks are particularly well-suited for image understanding problems, and present case studies in indoor-outdoor scene classification and parts-based object detection. First, improved scene classification is accomplished using both low-level features, such as color and texture, and semantic features, such as the presence of sky and grass. Integration of low-level and semantic features is achieved using a Bayesian network framework. The network structure can be determined by expert opinion or by automated structure learning methods. Second, object detection at multiple views relies on a parts-based approach, where specialized detectors locate object parts and a Bayesian network acts as the arbitrator in order to determine the object presence. In general, Bayesian networks are found to be powerful integrators of different features and help improve the performance of image understanding systems.

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