Falcon Optimization Algorithm for Bayesian Networks Structure Learning

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 Using Elephant Swarm Water Search Algorithm

International Journal of Swarm Intelligence Research ◽

10.4018/ijsir.2020040102 ◽

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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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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Bayesian Networks for Image Understanding

Bayesian Network Technologies ◽

10.4018/978-1-59904-141-4.ch007 ◽

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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A Bayesian Networks Structure Learning and Reasoning-Based Byproduct Gas Scheduling in Steel Industry

IEEE Transactions on Automation Science and Engineering ◽

10.1109/tase.2013.2277661 ◽

2014 ◽

Vol 11 (4) ◽

pp. 1149-1154 ◽

Cited By ~ 20

Author(s):

Jun Zhao ◽

Wei Wang ◽

Kan Sun ◽

Ying Liu

Keyword(s):

Bayesian Networks ◽

Steel Industry ◽

Structure Learning ◽

Byproduct Gas ◽

Networks Structure

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Analyzing the Effects of the Dwelling Characteristics on Space Heating Costs with the Rank Correlation Bayesian Network Model

10.21203/rs.3.rs-436208/v1 ◽

2021 ◽

Author(s):

Volkan Sevinç

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

Network Model ◽

Building Materials ◽

Structure Learning ◽

Rank Correlation ◽

Space Heating ◽

Bayesian Network Model ◽

Heating Systems ◽

External Wall

Abstract Energy is one of the main concerns of humanity because energy resources are limited and costly. In order to reduce the costs and to use the energy for space heating effectively, new building materials, techniques and insulations facilities are being developed. Therefore, it is important to know which factors affect the space heating costs. This study aims to introduce the novel Rank Correlation Bayesian Network model and its application in analyzing the effects of dwelling characteristics on the space heating costs. The results show that the constructed Rank Correlation Bayesian Network model performed better than the Bayesian networks models estimated by Bayesian search, PC and Greedy Thick Thinning algorithms, which are kinds of structure learning algorithms having different kinds of estimation mechanisms to build Bayesian networks. The constructed Rank Correlation Bayesian Network model shows that the space heating costs of the dwellings are mostly affected by the heating systems used. Coal stoves, air conditioners and electric stoves appear to be the costliest heating systems. The second most important factor appears to be the existence of external wall insulation. The lack of external wall insulation almost doubles the space heating costs. The third most important factor is the building age. Dwellings on the ground floors and the first floors appear to pay the highest space heating costs. Therefore, dwellings on these floors need to be more effectively insulated. As the size of the dwelling increases the heating cost increases too. Another result is that facing directions and floor levels of the dwellings have the least effects on their space heating.

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A Bayesian Network Structure Learning Approach to Identify Genes Associated with Stress in Spleens of Chickens

10.21203/rs.3.rs-1181777/v1 ◽

2021 ◽

Author(s):

E. A. Videla Rodriguez ◽

John B.O. Mitchell ◽

V. Anne Smith

Keyword(s):

Bayesian Networks ◽

Bayesian Network ◽

Stress Condition ◽

Simulated Annealing Algorithm ◽

Structure Learning ◽

Expression Patterns ◽

Bioinformatic Analysis ◽

Stress Conditions ◽

Bioinformatics Analyses ◽

Combining Data

Abstract Differences in the expression patterns of genes have been used to measure the effects of non-stress or stress conditions in poultry species. However, the list of genes identified can be extensive and they might be related to several biological systems. Therefore, the aim of this study was to identify a small set of genes closely associated with stress in a poultry animal model, the chicken (Gallus gallus), by reusing and combining data previously published together with bioinformatic analysis and Bayesian networks in a multi-step approach. Two datasets were collected from publicly available repositories and pre-processed. Bioinformatics analyses were performed to identify genes common to both datasets that showed differential expression patterns between non-stress and stress conditions. Bayesian networks were learnt using a Simulated Annealing algorithm implemented in the software Banjo. The structure of the Bayesian network consisted of 16 out of 19 genes in addition to the stress condition. CARD19 displayed a direct relationship with the stress condition, and three other genes, CYGB, BRAT1, and EPN3 were also relevant for the stress condition. The biological functionality of these genes are related to damage, apoptosis, and oxygen provision, and they could potentially be further explored as biomarkers of stress.

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