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|Title:||An ensemble of decision trees with random vector functional link networks for multi-class classification||Authors:||Katuwal, Rakesh
Suganthan, Ponnuthurai Nagaratnam
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2018||Source:||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||Journal:||Applied Soft Computing||Abstract:||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.||URI:||https://hdl.handle.net/10356/143804||ISSN:||1568-4946||DOI:||10.1016/j.asoc.2017.09.020||Rights:||© 2017 Elsevier B.V. All rights reserved.||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Journal Articles|
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