Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection
COVID-19 cases attract most computer science researchers. There are two popular learning approaches: Machine Learning (ML) and Deep Learning (DL). The approach was applied as a computer-based COVID-19 diagnosis. Most researchers prefer ensemble learning used to assist the process. The technique has...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
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2022
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| Online Access: | https://repository.ugm.ac.id/282182/1/FAdli%20et%20al%20-%202022%20-%20Performance%20Review%20of%20Ensemble%20Learning%20Method.pdf |
| _version_ | 1826050502569230336 |
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| author | Fadli, Vira Fitriza Soesanti, Indah Nugroho, Hanung Adi |
| author_facet | Fadli, Vira Fitriza Soesanti, Indah Nugroho, Hanung Adi |
| author_sort | Fadli, Vira Fitriza |
| collection | UGM |
| description | COVID-19 cases attract most computer science researchers. There are two popular learning approaches: Machine Learning (ML) and Deep Learning (DL). The approach was applied as a computer-based COVID-19 diagnosis. Most researchers prefer ensemble learning used to assist the process. The technique has various features and performance results. Based on the survey, there are several efforts to improve performance better. This review describes a brief of the ensemble approach. The ensemble applies to image classification. The application employs X-Ray and Computerized Tomography (CT) images. The technique should consider various ensemble strategies. As supportive evidence, a brief description of each method is presented in the table. This study shows all ensemble methods demonstrate to improve prediction results. The stacking ensemble becomes a method that achieves the highest performance. © 2022 IEEE. |
| first_indexed | 2024-03-14T00:05:00Z |
| format | Conference or Workshop Item |
| id | oai:generic.eprints.org:282182 |
| institution | Universiti Gadjah Mada |
| language | English |
| last_indexed | 2024-03-14T00:05:00Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | oai:generic.eprints.org:2821822023-11-28T08:33:28Z https://repository.ugm.ac.id/282182/ Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection Fadli, Vira Fitriza Soesanti, Indah Nugroho, Hanung Adi Electrical and Electronic Engineering not elsewhere classified COVID-19 cases attract most computer science researchers. There are two popular learning approaches: Machine Learning (ML) and Deep Learning (DL). The approach was applied as a computer-based COVID-19 diagnosis. Most researchers prefer ensemble learning used to assist the process. The technique has various features and performance results. Based on the survey, there are several efforts to improve performance better. This review describes a brief of the ensemble approach. The ensemble applies to image classification. The application employs X-Ray and Computerized Tomography (CT) images. The technique should consider various ensemble strategies. As supportive evidence, a brief description of each method is presented in the table. This study shows all ensemble methods demonstrate to improve prediction results. The stacking ensemble becomes a method that achieves the highest performance. © 2022 IEEE. 2022 Conference or Workshop Item PeerReviewed application/pdf en https://repository.ugm.ac.id/282182/1/FAdli%20et%20al%20-%202022%20-%20Performance%20Review%20of%20Ensemble%20Learning%20Method.pdf Fadli, Vira Fitriza and Soesanti, Indah and Nugroho, Hanung Adi (2022) Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection. In: IEEE International Conference of Computer Science and Information Technology (ICOSNIKOM). https://ieeexplore.ieee.org/document/10034928 |
| spellingShingle | Electrical and Electronic Engineering not elsewhere classified Fadli, Vira Fitriza Soesanti, Indah Nugroho, Hanung Adi Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection |
| title | Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection |
| title_full | Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection |
| title_fullStr | Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection |
| title_full_unstemmed | Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection |
| title_short | Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection |
| title_sort | performance review of ensemble learning method use in covid 19 case detection |
| topic | Electrical and Electronic Engineering not elsewhere classified |
| url | https://repository.ugm.ac.id/282182/1/FAdli%20et%20al%20-%202022%20-%20Performance%20Review%20of%20Ensemble%20Learning%20Method.pdf |
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