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dc.contributor.authorShi, Qiushien_US
dc.contributor.authorKatuwal, Rakeshen_US
dc.contributor.authorSuganthan, Ponnuthurai Nagaratnamen_US
dc.contributor.authorTanveer, M.en_US
dc.identifier.citationShi, Q., Katuwal, R., Suganthan, P. N. & Tanveer, M. (2021). Random vector functional link neural network based ensemble deep learning. Pattern Recognition, 117, 107978-.
dc.description.abstractIn this paper, we propose deep learning frameworks based on the randomized neural network. Inspired by the principles of Random Vector Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) with stacked layers. The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the output weights are computed using the closed-form solution as in a standard RVFL network. We also propose an ensemble deep network (edRVFL) that can be regarded as a marriage of ensemble learning with deep learning. Unlike traditional ensembling approaches that require training several models independently from scratch, edRVFL is obtained by training a single dRVFL network once. Both dRVFL and edRVFL frameworks are generic and can be used with any RVFL variant. To illustrate this, we integrate the deep learning RVFL networks with a recently proposed sparse pre-trained RVFL (SP-RVFL). Experiments on 46 tabular UCI classification datasets and 12 sparse datasets demonstrate that the proposed deep RVFL networks outperform state-of-the-art deep feed-forward neural networks (FNNs).en_US
dc.relation.ispartofPattern Recognitionen_US
dc.rights© 2021 Elsevier Ltd. All rights reserved.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleRandom vector functional link neural network based ensemble deep learningen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.subject.keywordsRandom Vector Functional Linken_US
dc.subject.keywordsEnsemble Deep Learningen_US
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