Designing multiple classifier combinations a survey
Classification accuracy can be improved through multiple classifier approach. It has been proven that multiple classifier combinations can successfully obtain better classification accuracy than using a single classifier. There are two main problems in designing a multiple classifier combination whi...
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| 主要な著者: | , |
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| フォーマット: | 論文 |
| 言語: | English |
| 出版事項: |
Little Lion Scientific
2019
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| 主題: | |
| オンライン･アクセス: | http://repo.uum.edu.my/27859/1/JTAIT%2097%2020%202019%202386%202405.pdf http://repo.uum.edu.my/27859/ http://www.jatit.org/volumes/ninetyseven20.php |
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| 要約: | Classification accuracy can be improved through multiple classifier approach. It has been proven that multiple classifier combinations can successfully obtain better classification accuracy than using a single classifier. There are two main problems in designing a multiple classifier combination which are determining the classifier ensemble and combiner construction. This paper reviews approaches in constructing the classifier ensemble and combiner. For each approach, methods have been reviewed and their advantages and disadvantages have been highlighted. A random strategy and majority voting are the most commonly used to construct the ensemble and combiner, respectively. The results presented in this
review are expected to be a road map in designing multiple classifier combinations. |
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