Random vector functional link neural network based ensemble deep learning.

• Inspired by the principles of Random Vector Functional Link (RVFL) network, we propose a deep RVFL network (dRVFL) with rich feature extraction capabilities through several hidden layers. • We also propose an ensemble deep network (edRVFL) based on a single dRVFL network. • We demonstrate the generic nature of the proposed methods by integrating them with a recent RVFL variant called sparse-pretrained RVFL (SP-RVFL). • Experiments on 46 tabular UCI classification datasets demonstrate that the proposed ensemble deep RVFL networks outperform state-of-the-art deep feed-forward neural networks. • Experiments on 12 sparse classification datasets demonstrate that the proposed ensemble deep SP-RVFL networks outperform the best. 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). [ABSTRACT FROM AUTHOR]