An ensemble of decision trees with random vector functional link networks for multi-class classification
Ensembles of decision trees and neural networks are popular choices for solving classification and regression problems. In this paper, a new ensemble of classifiers that consists of decision trees and random vector functional link network is proposed for multi-class classification. The random vector...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Article |
| 語言: | English |
| 出版: |
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/143804 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | Ensembles of decision trees and neural networks are popular choices for solving classification and regression problems. In this paper, a new ensemble of classifiers that consists of decision trees and random vector functional link network is proposed for multi-class classification. The random vector functional link network (RVFL) partitions the original training samples into K distinct subsets, where K is the number of classes in a data set, and a decision tree is induced for each subset. Both univariate and multivariate (oblique) decision trees are used with RVFL. The performance of the proposed method is evaluated on 65 multi-class UCI datasets. The results demonstrate that the classification accuracy of the proposed ensemble method is significantly better than other state-of-the-art classifiers for medium and large sized data sets. |
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