Please use this identifier to cite or link to this item:
Title: An Ensemble of Kernel Ridge Regression for Multi-class Classification
Authors: Suganthan, Ponnuthurai Nagaratnam
Rakesh, Katuwal
Keywords: Kernel Ridge Regression
Multi-class Classification
Issue Date: 2017
Source: Rakesh, K., & Suganthan, P. N. (2017). An Ensemble of Kernel Ridge Regression for Multi-class Classification. Procedia Computer Science, 108, 375-383.
Series/Report no.: Procedia Computer Science
Abstract: We propose an ensemble of kernel ridge regression based classifiers in this paper. Kernel ridge regression admits a closed form solution making it faster to compute and also making it suitable to use for ensemble methods for small and medium sized data sets. Our method uses random vector functional link network to generate training samples for kernel ridge regression classifiers. Several kernel ridge regression classifiers are constructed from different training subsets in each base classifier. The partitioning of the training samples into different subsets leads to a reduction in computational complexity when calculating matrix inverse compared with the standard approach of using all N samples for kernel matrix inversion. The proposed method is evaluated using well known multi-class UCI data sets. Experimental results show the proposed ensemble method outperforms the single kernel ridge regression classifier and its bagging version.
ISSN: 1877-0509
DOI: 10.1016/j.procs.2017.05.109
Rights: © 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Journal Articles

Files in This Item:
File Description SizeFormat 
An Ensemble of Kernel Ridge Regression for Multi-class Classification.pdf427.48 kBAdobe PDFThumbnail

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.