Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification
Classification analysis is a supervised learning method that can be utilized to categorize levels of greenhouse gas emissions. Regular monitoring of greenhouse gas emissions is essential for relevant agencies to devise prevention and mitigation programs that address climate change. In classification...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/1919/ http://eprints.intimal.edu.my/1919/1/jods2024_05.pdf |
| _version_ | 1848766871500750848 |
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| author | Riko, Febrian Anne Mudya, Yolanda |
| author_facet | Riko, Febrian Anne Mudya, Yolanda |
| author_sort | Riko, Febrian |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | Classification analysis is a supervised learning method that can be utilized to categorize levels of greenhouse gas emissions. Regular monitoring of greenhouse gas emissions is essential for relevant agencies to devise prevention and mitigation programs that address climate change. In classification analysis, enhancing model performance is correlated with the number of features or variables utilized, thus necessitating feature selection in its application. This study compares feature selection methods for classifying greenhouse gas emission levels, specifically wrapper feature selection, recursive feature elimination, and boruta. The Support Vector Machine (SVM) algorithm is employed to evaluate classification performance, focusing on binary classification into "high" and "low" categories in this study. The results indicate that classification performance improves with feature selection and recursive feature elimination compared to scenarios without feature selection or with Boruta feature selection. By employing three out of the thirty-nine features, accuracy, sensitivity, and specificity of 98.95%, 99%, and 97% were achieved, respectively. |
| first_indexed | 2025-11-14T11:58:02Z |
| format | Article |
| id | intimal-1919 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:58:02Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
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| spelling | intimal-19192024-06-04T03:40:24Z http://eprints.intimal.edu.my/1919/ Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification Riko, Febrian Anne Mudya, Yolanda Q Science (General) QA Mathematics QA75 Electronic computers. Computer science Classification analysis is a supervised learning method that can be utilized to categorize levels of greenhouse gas emissions. Regular monitoring of greenhouse gas emissions is essential for relevant agencies to devise prevention and mitigation programs that address climate change. In classification analysis, enhancing model performance is correlated with the number of features or variables utilized, thus necessitating feature selection in its application. This study compares feature selection methods for classifying greenhouse gas emission levels, specifically wrapper feature selection, recursive feature elimination, and boruta. The Support Vector Machine (SVM) algorithm is employed to evaluate classification performance, focusing on binary classification into "high" and "low" categories in this study. The results indicate that classification performance improves with feature selection and recursive feature elimination compared to scenarios without feature selection or with Boruta feature selection. By employing three out of the thirty-nine features, accuracy, sensitivity, and specificity of 98.95%, 99%, and 97% were achieved, respectively. INTI International University 2024-05 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/1919/1/jods2024_05.pdf Riko, Febrian and Anne Mudya, Yolanda (2024) Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification. Journal of Data Science, 2024 (05). pp. 1-8. ISSN 2805-5160 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | Q Science (General) QA Mathematics QA75 Electronic computers. Computer science Riko, Febrian Anne Mudya, Yolanda Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification |
| title | Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification |
| title_full | Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification |
| title_fullStr | Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification |
| title_full_unstemmed | Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification |
| title_short | Comparison of Recursive Feature Elimination and Boruta as Feature Selection in Greenhouse Gas Emission Data Classification |
| title_sort | comparison of recursive feature elimination and boruta as feature selection in greenhouse gas emission data classification |
| topic | Q Science (General) QA Mathematics QA75 Electronic computers. Computer science |
| url | http://eprints.intimal.edu.my/1919/ http://eprints.intimal.edu.my/1919/ http://eprints.intimal.edu.my/1919/1/jods2024_05.pdf |