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

本文提出了基于 Ensemble Deep Random Vector Functional Link (edRVFL) 的加权 edRVFL (WedRVFL) 和基于修剪的 edRVFL (PedRVFL) 网络，同时也提出了加权和修剪的 edRVFL 网络 (WPedRVFL)。实验结果表明，我们提出的方法在24个表格型UCI分类数据集上表现出超越其他最先进的深度前馈神经网络(FNN)的性能。

基于加权和修剪的集成深度随机向量功能链接网络用于表格数据分类