New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm
Feature selection is crucial for minimizing redundancy in information and addressing the limitations of traditional classification methods when dealing with large datasets and numerous features in many machine learning applications. To improve the classification, this article introduced two hybrid m...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2024-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2024/7112770 |
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| _version_ | 1846166280596357120 |
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| author | Khadijeh Nemati Amir Hosein Refahi Sheikhani Sohrab Kordrostami Kamrad Khoshhal Roudposhti |
| author_facet | Khadijeh Nemati Amir Hosein Refahi Sheikhani Sohrab Kordrostami Kamrad Khoshhal Roudposhti |
| author_sort | Khadijeh Nemati |
| collection | DOAJ |
| description | Feature selection is crucial for minimizing redundancy in information and addressing the limitations of traditional classification methods when dealing with large datasets and numerous features in many machine learning applications. To improve the classification, this article introduced two hybrid methods utilizing a genetic algorithm and a gray wolf algorithm with structured dispersion norms for feature selection. These techniques involved the utilization of a genetic algorithm and a gray wolf algorithm for feature selection. The features selected by these algorithms were used in the classification process by employing a two-layer perceptron as a classifier. The novel sparse norm is employed to assess and compute classification errors in these methodologies. To assess the effectiveness of the suggested techniques, they were compared with the existing feature selection methods using various publicly accessible datasets. The results of the experiments consistently demonstrate that the proposed methods outperform other approaches. |
| format | Article |
| id | doaj-art-912ad3518c1140cf96eed02b7761614f |
| institution | Kabale University |
| issn | 2090-0155 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-912ad3518c1140cf96eed02b7761614f2024-11-16T00:00:03ZengWileyJournal of Electrical and Computer Engineering2090-01552024-01-01202410.1155/2024/7112770New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity NormKhadijeh Nemati0Amir Hosein Refahi Sheikhani1Sohrab Kordrostami2Kamrad Khoshhal Roudposhti3Department of Applied Mathematics and Computer ScienceDepartment of Applied Mathematics and Computer ScienceDepartment of Applied Mathematics and Computer ScienceDepartment of Computer EngineeringFeature selection is crucial for minimizing redundancy in information and addressing the limitations of traditional classification methods when dealing with large datasets and numerous features in many machine learning applications. To improve the classification, this article introduced two hybrid methods utilizing a genetic algorithm and a gray wolf algorithm with structured dispersion norms for feature selection. These techniques involved the utilization of a genetic algorithm and a gray wolf algorithm for feature selection. The features selected by these algorithms were used in the classification process by employing a two-layer perceptron as a classifier. The novel sparse norm is employed to assess and compute classification errors in these methodologies. To assess the effectiveness of the suggested techniques, they were compared with the existing feature selection methods using various publicly accessible datasets. The results of the experiments consistently demonstrate that the proposed methods outperform other approaches.http://dx.doi.org/10.1155/2024/7112770 |
| spellingShingle | Khadijeh Nemati Amir Hosein Refahi Sheikhani Sohrab Kordrostami Kamrad Khoshhal Roudposhti New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm Journal of Electrical and Computer Engineering |
| title | New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm |
| title_full | New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm |
| title_fullStr | New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm |
| title_full_unstemmed | New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm |
| title_short | New Hybrid Feature Selection Approaches Based on ANN and Novel Sparsity Norm |
| title_sort | new hybrid feature selection approaches based on ann and novel sparsity norm |
| url | http://dx.doi.org/10.1155/2024/7112770 |
| work_keys_str_mv | AT khadijehnemati newhybridfeatureselectionapproachesbasedonannandnovelsparsitynorm AT amirhoseinrefahisheikhani newhybridfeatureselectionapproachesbasedonannandnovelsparsitynorm AT sohrabkordrostami newhybridfeatureselectionapproachesbasedonannandnovelsparsitynorm AT kamradkhoshhalroudposhti newhybridfeatureselectionapproachesbasedonannandnovelsparsitynorm |