Weighting and Pruning based Ensemble Deep Random Vector Functional Link Network for Tabular Data Classification
This work addresses classification challenges in tabular data, which is incremental as it builds upon existing edRVFL methods with specific enhancements.
The authors tackled the problem of improving classification accuracy for tabular data by introducing batch normalization, weighting, and pruning techniques to the Ensemble Deep Random Vector Functional Link (edRVFL) network, resulting in superior performance compared to state-of-the-art deep feedforward neural networks on 24 UCI datasets.
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.