A Comparison Study About Parameter Optimization Using Swarm Algorithms
Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not triv...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9775108/ |
| _version_ | 1811231984192585728 |
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| author | Halcyon Davys Pereira De Carvalho Wedson L. Soares Wylliams Barbosa Santos Roberta Fagundes |
| author_facet | Halcyon Davys Pereira De Carvalho Wedson L. Soares Wylliams Barbosa Santos Roberta Fagundes |
| author_sort | Halcyon Davys Pereira De Carvalho |
| collection | DOAJ |
| description | Adjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to adjust the models’ parameters automatically. These models were built around two machine learning-based algorithms, an extreme learning machine neural network, and a support vector regression. The optimization algorithms used are Particle Swarm Optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets in different contexts indicated that optimized algorithms models perform better in convergence, precision, and robustness than the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool for analyzing different data contexts. Thus, this study shows that the optimized algorithm models (in particular the ELM PSO model) are more accurate than all experimental evaluations. |
| first_indexed | 2024-04-12T10:55:39Z |
| format | Article |
| id | doaj.art-776edd11f9c14ca79458423fa8e8c01a |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-12T10:55:39Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-776edd11f9c14ca79458423fa8e8c01a2022-12-22T03:36:06ZengIEEEIEEE Access2169-35362022-01-0110554885549810.1109/ACCESS.2022.31752029775108A Comparison Study About Parameter Optimization Using Swarm AlgorithmsHalcyon Davys Pereira De Carvalho0https://orcid.org/0000-0001-8933-5912Wedson L. Soares1https://orcid.org/0000-0002-0078-3944Wylliams Barbosa Santos2https://orcid.org/0000-0003-2578-1248Roberta Fagundes3https://orcid.org/0000-0002-7172-4183Department of Computer Engineering, University of Pernambuco, Recife, BrazilDepartment of Computer Engineering, University of Pernambuco, Recife, BrazilDepartment of Computer Engineering, University of Pernambuco, Recife, BrazilDepartment of Computer Engineering, University of Pernambuco, Recife, BrazilAdjusting the parameters of a machine learning algorithm can be difficult if the possible domain of expansion of these parameters is too high. In addition, if a sensible parameter is not adjusted correctly, the changes can be very impactful in the final results, making adjusting it manually not trivial. In order to adjust these features automatically, the current work proposes six models based on the use of optimization algorithms to adjust the models’ parameters automatically. These models were built around two machine learning-based algorithms, an extreme learning machine neural network, and a support vector regression. The optimization algorithms used are Particle Swarm Optimization, the Artificial Bee Colony, and the genetic algorithm. The models were compared with each other based on predictive precision in the criterion of Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and statistical tests. The experimental results on ten datasets in different contexts indicated that optimized algorithms models perform better in convergence, precision, and robustness than the non-optimized algorithms models. Therefore, the automatic adjustment of the parameters of optimized algorithms is a powerful tool for analyzing different data contexts. Thus, this study shows that the optimized algorithm models (in particular the ELM PSO model) are more accurate than all experimental evaluations.https://ieeexplore.ieee.org/document/9775108/Machine learningextreme learning machinesupport vector regressionensembleoptimization algorithm |
| spellingShingle | Halcyon Davys Pereira De Carvalho Wedson L. Soares Wylliams Barbosa Santos Roberta Fagundes A Comparison Study About Parameter Optimization Using Swarm Algorithms IEEE Access Machine learning extreme learning machine support vector regression ensemble optimization algorithm |
| title | A Comparison Study About Parameter Optimization Using Swarm Algorithms |
| title_full | A Comparison Study About Parameter Optimization Using Swarm Algorithms |
| title_fullStr | A Comparison Study About Parameter Optimization Using Swarm Algorithms |
| title_full_unstemmed | A Comparison Study About Parameter Optimization Using Swarm Algorithms |
| title_short | A Comparison Study About Parameter Optimization Using Swarm Algorithms |
| title_sort | comparison study about parameter optimization using swarm algorithms |
| topic | Machine learning extreme learning machine support vector regression ensemble optimization algorithm |
| url | https://ieeexplore.ieee.org/document/9775108/ |
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