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In this paper, we first introduce batch normalization to the edRVFL network.This re-normalization method can help the network avoid divergence of thehidden features. Then we propose novel variants of Ensemble Deep Random VectorFunctional Link (edRVFL). Weighted edRVFL (WedRVFL) uses weighting methods togive training samples different weights in different layers according to howthe samples were classified confidently in the previous layer therebyincreasing the ensemble's diversity and accuracy. Furthermore, a pruning-basededRVFL (PedRVFL) has also been proposed. We prune some inferior neurons basedon their importance for classification before generating the next hidden layer.Through this method, we ensure that the randomly generated inferior featureswill not propagate to deeper layers. Subsequently, the combination of weightingand pruning, called Weighting and Pruning based Ensemble Deep Random VectorFunctional Link Network (WPedRVFL), is proposed. We compare their performanceswith other state-of-the-art deep feedforward neural networks (FNNs) on 24tabular UCI classification datasets. The experimental results illustrate thesuperior performance of our proposed methods.

Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification