A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm
Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem....
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Universidad Internacional de La Rioja (UNIR)
2023-06-01
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| Series: | International Journal of Interactive Multimedia and Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.ijimai.org/journal/bibcite/reference/3244 |
| _version_ | 1797810733157711872 |
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| author | Hoshang Qasim Awla Shahab Wahhab Kareem Amin Salih Mohammed |
| author_facet | Hoshang Qasim Awla Shahab Wahhab Kareem Amin Salih Mohammed |
| author_sort | Hoshang Qasim Awla |
| collection | DOAJ |
| description | Bayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem. The score and search technique is one Bayesian Network structure learning strategy. In Bayesian network structure learning the Falcon Optimization Algorithm (FOA) is presented and evaluated by the authors. Inserting, Reversing, Moving, and Deleting, are used in the method to create the FOA for finding the best structural solution. The FOA algorithm is based on the falcon's searching technique during drought conditions. The suggested technique is compared to the score metric function of Pigeon Inspired search algorithm, Greedy Search, and Antlion optimization search algorithm. The performance of these techniques in terms of confusion matrices was further evaluated by the authors using a variety of benchmark data sets. The Falcon optimization algorithm outperforms the previous algorithms and generates higher scores and accuracy values, as evidenced by the results of our experiments. |
| first_indexed | 2024-03-13T07:12:14Z |
| format | Article |
| id | doaj.art-c43e0be9dd4141cc88f10dfd05ac9071 |
| institution | Directory Open Access Journal |
| issn | 1989-1660 |
| language | English |
| last_indexed | 2024-03-13T07:12:14Z |
| publishDate | 2023-06-01 |
| publisher | Universidad Internacional de La Rioja (UNIR) |
| record_format | Article |
| series | International Journal of Interactive Multimedia and Artificial Intelligence |
| spelling | doaj.art-c43e0be9dd4141cc88f10dfd05ac90712023-06-05T20:31:39ZengUniversidad Internacional de La Rioja (UNIR)International Journal of Interactive Multimedia and Artificial Intelligence1989-16602023-06-0182818710.9781/ijimai.2023.01.004ijimai.2023.01.004A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization AlgorithmHoshang Qasim AwlaShahab Wahhab KareemAmin Salih MohammedBayesian networks are analytical models that may represent probabilistic dependent connections among variables and are useful in machine learning for generating knowledge structure. Due to the vastness of the solution space, learning Bayesian network (BN) structures from data is an NP-hard problem. The score and search technique is one Bayesian Network structure learning strategy. In Bayesian network structure learning the Falcon Optimization Algorithm (FOA) is presented and evaluated by the authors. Inserting, Reversing, Moving, and Deleting, are used in the method to create the FOA for finding the best structural solution. The FOA algorithm is based on the falcon's searching technique during drought conditions. The suggested technique is compared to the score metric function of Pigeon Inspired search algorithm, Greedy Search, and Antlion optimization search algorithm. The performance of these techniques in terms of confusion matrices was further evaluated by the authors using a variety of benchmark data sets. The Falcon optimization algorithm outperforms the previous algorithms and generates higher scores and accuracy values, as evidenced by the results of our experiments.https://www.ijimai.org/journal/bibcite/reference/3244bayesian networkoptimization search algorithmsearchstructure learning |
| spellingShingle | Hoshang Qasim Awla Shahab Wahhab Kareem Amin Salih Mohammed A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm International Journal of Interactive Multimedia and Artificial Intelligence bayesian network optimization search algorithm search structure learning |
| title | A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm |
| title_full | A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm |
| title_fullStr | A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm |
| title_full_unstemmed | A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm |
| title_short | A Comparative Evaluation of Bayesian Networks Structure Learning Using Falcon Optimization Algorithm |
| title_sort | comparative evaluation of bayesian networks structure learning using falcon optimization algorithm |
| topic | bayesian network optimization search algorithm search structure learning |
| url | https://www.ijimai.org/journal/bibcite/reference/3244 |
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