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

• 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
• Institution: Nanyang Technological University

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.