A Jaya algorithm based wrapper method for optimal feature selection in supervised classification
In recent years, Jaya optimization algorithm has been successfully applied in several optimization problems. This paper presents a novel feature selection (FS) approach based on Jaya optimization algorithm (FSJaya) along with supervised machine learning techniques to select the optimal features. Thi...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157820303670 |
| _version_ | 1829462101866840064 |
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| author | Himansu Das Bighnaraj Naik H.S. Behera |
| author_facet | Himansu Das Bighnaraj Naik H.S. Behera |
| author_sort | Himansu Das |
| collection | DOAJ |
| description | In recent years, Jaya optimization algorithm has been successfully applied in several optimization problems. This paper presents a novel feature selection (FS) approach based on Jaya optimization algorithm (FSJaya) along with supervised machine learning techniques to select the optimal features. This approach uses a search technique to find the best suitable features by updating the worst features to reduce the dimensions of the feature space. This improves the performance of supervised machine learning techniques. The effectiveness of the proposed approach is evaluated for ten benchmark datasets and compared with several FS approaches such as FS using genetic algorithm (FSGA), FS using particle swarm optimization algorithm (FSPSO), and FS using differential evolutionary (FSDE). The experimental result has shown that the average classification accuracy of FSJaya on most of the datasets is superior over the existing methods such as FSGA, FSPSO, and FSDE. The proof of statistical significance of the proposed approach has been validated by using Friedman and Holm test. This proposed approach is found efficient in selecting an optimal subset of features as compared to other counterparts. |
| first_indexed | 2024-12-12T12:02:45Z |
| format | Article |
| id | doaj.art-500ac450d67b45968bf8876b95b2c7d5 |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-12-12T12:02:45Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-500ac450d67b45968bf8876b95b2c7d52022-12-22T00:25:04ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782022-06-0134638513863A Jaya algorithm based wrapper method for optimal feature selection in supervised classificationHimansu Das0Bighnaraj Naik1H.S. Behera2Department of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, India; Corresponding author.Department of Computer Application, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, IndiaDepartment of Information Technology, Veer Surendra Sai University of Technology, Burla, Sambalpur 768018, Odisha, IndiaIn recent years, Jaya optimization algorithm has been successfully applied in several optimization problems. This paper presents a novel feature selection (FS) approach based on Jaya optimization algorithm (FSJaya) along with supervised machine learning techniques to select the optimal features. This approach uses a search technique to find the best suitable features by updating the worst features to reduce the dimensions of the feature space. This improves the performance of supervised machine learning techniques. The effectiveness of the proposed approach is evaluated for ten benchmark datasets and compared with several FS approaches such as FS using genetic algorithm (FSGA), FS using particle swarm optimization algorithm (FSPSO), and FS using differential evolutionary (FSDE). The experimental result has shown that the average classification accuracy of FSJaya on most of the datasets is superior over the existing methods such as FSGA, FSPSO, and FSDE. The proof of statistical significance of the proposed approach has been validated by using Friedman and Holm test. This proposed approach is found efficient in selecting an optimal subset of features as compared to other counterparts.http://www.sciencedirect.com/science/article/pii/S1319157820303670Feature selectionSupervised classificationJayaOptimization algorithmWrapper method |
| spellingShingle | Himansu Das Bighnaraj Naik H.S. Behera A Jaya algorithm based wrapper method for optimal feature selection in supervised classification Journal of King Saud University: Computer and Information Sciences Feature selection Supervised classification Jaya Optimization algorithm Wrapper method |
| title | A Jaya algorithm based wrapper method for optimal feature selection in supervised classification |
| title_full | A Jaya algorithm based wrapper method for optimal feature selection in supervised classification |
| title_fullStr | A Jaya algorithm based wrapper method for optimal feature selection in supervised classification |
| title_full_unstemmed | A Jaya algorithm based wrapper method for optimal feature selection in supervised classification |
| title_short | A Jaya algorithm based wrapper method for optimal feature selection in supervised classification |
| title_sort | jaya algorithm based wrapper method for optimal feature selection in supervised classification |
| topic | Feature selection Supervised classification Jaya Optimization algorithm Wrapper method |
| url | http://www.sciencedirect.com/science/article/pii/S1319157820303670 |
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