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...
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/143804 |
| _version_ | 1811688967294156800 |
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| author | Katuwal, Rakesh Suganthan, Ponnuthurai Nagaratnam Zhang, Le |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Katuwal, Rakesh Suganthan, Ponnuthurai Nagaratnam Zhang, Le |
| author_sort | Katuwal, Rakesh |
| collection | NTU |
| description | 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. |
| first_indexed | 2024-10-01T05:40:37Z |
| format | Journal Article |
| id | ntu-10356/143804 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:40:37Z |
| publishDate | 2020 |
| record_format | dspace |
| spelling | ntu-10356/1438042020-09-24T06:47:31Z An ensemble of decision trees with random vector functional link networks for multi-class classification Katuwal, Rakesh Suganthan, Ponnuthurai Nagaratnam Zhang, Le School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Random Forest Oblique Random Forest 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. 2020-09-24T06:47:30Z 2020-09-24T06:47:30Z 2018 Journal Article Katuwal, R., Suganthan P. N., & Zhang, Le. (2018). An ensemble of decision trees with random vector functional link networks for multi-class classification. Applied Soft Computing, 70, 1146-1153. 10.1016/j.asoc.2017.09.020 1568-4946 https://hdl.handle.net/10356/143804 10.1016/j.asoc.2017.09.020 70 1146 1153 en Applied Soft Computing © 2017 Elsevier B.V. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Random Forest Oblique Random Forest Katuwal, Rakesh Suganthan, Ponnuthurai Nagaratnam Zhang, Le An ensemble of decision trees with random vector functional link networks for multi-class classification |
| title | An ensemble of decision trees with random vector functional link networks for multi-class classification |
| title_full | An ensemble of decision trees with random vector functional link networks for multi-class classification |
| title_fullStr | An ensemble of decision trees with random vector functional link networks for multi-class classification |
| title_full_unstemmed | An ensemble of decision trees with random vector functional link networks for multi-class classification |
| title_short | An ensemble of decision trees with random vector functional link networks for multi-class classification |
| title_sort | ensemble of decision trees with random vector functional link networks for multi class classification |
| topic | Engineering::Electrical and electronic engineering Random Forest Oblique Random Forest |
| url | https://hdl.handle.net/10356/143804 |
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