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:	Katuwal, Rakesh, Suganthan, Ponnuthurai Nagaratnam, Zhang, Le
Other Authors:	School of Electrical and Electronic Engineering
Format:	Article
Language:	English
Published:	2020
Subjects:	Engineering::Electrical and electronic engineering Random Forest Oblique Random Forest
Online Access:	https://hdl.handle.net/10356/143804
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-143804
record_format	dspace
spelling	sg-ntu-dr.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.
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	Engineering::Electrical and electronic engineering Random Forest Oblique Random Forest
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
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.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Katuwal, Rakesh Suganthan, Ponnuthurai Nagaratnam Zhang, Le
format	Article
author	Katuwal, Rakesh Suganthan, Ponnuthurai Nagaratnam Zhang, Le
author_sort	Katuwal, Rakesh
title	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_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_sort	ensemble of decision trees with random vector functional link networks for multi-class classification
publishDate	2020
url	https://hdl.handle.net/10356/143804
_version_	1681057964151013376

An ensemble of decision trees with random vector functional link networks for multi-class classification

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