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Performance Review of Ensemble Learning Method Use in COVID-19 Case Detection
4th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2258591
ABSTRACT
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.
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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2022 Year: 2022 Document Type: Article

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Full text: Available Collection: Databases of international organizations Database: Scopus Language: English Journal: 4th IEEE International Conference of Computer Science and Information Technology, ICOSNIKOM 2022 Year: 2022 Document Type: Article