Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/88058
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dc.contributor.authorSuganthan, Ponnuthurai Nagaratnamen
dc.contributor.authorRakesh, Katuwalen
dc.date.accessioned2018-03-15T05:22:19Zen
dc.date.accessioned2019-12-06T16:55:07Z-
dc.date.available2018-03-15T05:22:19Zen
dc.date.available2019-12-06T16:55:07Z-
dc.date.issued2017en
dc.identifier.citationRakesh, K., & Suganthan, P. N. (2017). An Ensemble of Kernel Ridge Regression for Multi-class Classification. Procedia Computer Science, 108, 375-383.en
dc.identifier.issn1877-0509en
dc.identifier.urihttps://hdl.handle.net/10356/88058-
dc.description.abstractWe 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.en
dc.format.extent9 p.en
dc.language.isoenen
dc.relation.ispartofseriesProcedia Computer Scienceen
dc.rights© 2017 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).en
dc.subjectKernel Ridge Regressionen
dc.subjectMulti-class Classificationen
dc.titleAn Ensemble of Kernel Ridge Regression for Multi-class Classificationen
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen
dc.identifier.doi10.1016/j.procs.2017.05.109en
dc.description.versionPublished versionen
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