mlsanityData to 14 Oct 2026

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

arXiv 2201.05809 ↗First posted 15 Jan 2022cs.LG cs.AI cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 14 of its 55 references that are in the map. First posted on arXiv 15 Jan 2022.

Builds on

14 of 55 references.

References · 14

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  12. 2020Random Vector Functional Link Networks for Function Approximation on Manifoldscited by 19
2 more
  1. 2021Random vector functional link neural network based ensemble deep learning for short-term load forecastingcited by 6
  2. 2018Regularization Learning Networks: Deep Learning for Tabular Datasetscited by 3

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Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2022Random vector functional link network: recent developments, applications, and future directionscited by 33
  2. 2023NCART: Neural Classification and Regression Tree for Tabular Datacited by 7
  3. 2022A Max-relevance-min-divergence Criterion for Data Discretization with Applications on Naive Bayescited by 1