Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/161420
Title: Random vector functional link neural network based ensemble deep learning
Authors: Shi, Qiushi
Katuwal, Rakesh
Suganthan, Ponnuthurai Nagaratnam
Tanveer, M.
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2021
Source: Shi, Q., Katuwal, R., Suganthan, P. N. & Tanveer, M. (2021). Random vector functional link neural network based ensemble deep learning. Pattern Recognition, 117, 107978-. https://dx.doi.org/10.1016/j.patcog.2021.107978
Journal: Pattern Recognition
Abstract: In 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).
URI: https://hdl.handle.net/10356/161420
ISSN: 0031-3203
DOI: 10.1016/j.patcog.2021.107978
Schools: School of Electrical and Electronic Engineering 
Rights: © 2021 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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