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

TL;DR

This paper enhances the edRVFL network for tabular data classification by introducing batch normalization, and proposes weighted, pruned, and combined ensemble variants, demonstrating superior performance on multiple datasets.

Contribution

The paper introduces novel weighted and pruning-based ensemble edRVFL variants, improving accuracy and diversity for tabular data classification.

Findings

Weighted edRVFL increases ensemble diversity.

Pruning removes inferior neurons to improve feature quality.

Combined weighting and pruning outperforms state-of-the-art methods.

Abstract

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.