# Random Vector Functional Link Neural Network based Ensemble Deep Learning

arXiv: https://arxiv.org/abs/1907.00350 · HTML: https://mlsanity.com/paper/1907.00350/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jun 2019.

## Built on by (most cited first, 20 of 20)

- [Ensemble Reinforcement Learning: A Survey](https://mlsanity.com/paper/2303.02618/) (2023)
- [Graph Embedded Intuitionistic Fuzzy Random Vector Functional Link Neural Network for Class Imbalance Learning](https://mlsanity.com/paper/2307.07881/) (2023)
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- [NCART: Neural Classification and Regression Tree for Tabular Data](https://mlsanity.com/paper/2307.12198/) (2023)
- [GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing](https://mlsanity.com/paper/2409.16735/) (2024)
- [Random vector functional link neural network based ensemble deep learning for short-term load forecasting](https://mlsanity.com/paper/2107.14385/) (2021)
- [Keeping Deep Learning Models in Check: A History-Based Approach to Mitigate Overfitting](https://mlsanity.com/paper/2401.10359/) (2024)
- [Decoding Cognitive Health Using Machine Learning: A Comprehensive Evaluation for Diagnosis of Significant Memory Concern](https://mlsanity.com/paper/2405.07070/) (2024)
- [Ensemble Deep Random Vector Functional Link Neural Network Based on Fuzzy Inference System](https://mlsanity.com/paper/2406.00801/) (2024)
- [GRVFL-MV: Graph Random Vector Functional Link Based on Multi-View Learning](https://mlsanity.com/paper/2409.04743/) (2024)
- [Enhanced Feature Based Granular Ball Twin Support Vector Machine](https://mlsanity.com/paper/2410.05786/) (2024)
- [CAWI: Copula-Aligned Weight Initialization for Randomized Neural Networks](https://mlsanity.com/paper/2605.12580/) (2026)
- [Rolling the dice for better deep learning performance: A study of randomness techniques in deep neural networks](https://mlsanity.com/paper/2404.03992/) (2024)
- [Incremental Online Learning of Randomized Neural Network with Forward Regularization](https://mlsanity.com/paper/2412.13096/) (2024)
- [Lightweight 3D Convolutional Neural Network for Schizophrenia diagnosis using MRI Images and Ensemble Bagging Classifier](https://mlsanity.com/paper/2211.02868/) (2022)
- [Partially Randomizing Transformer Weights for Dialogue Response Diversity](https://mlsanity.com/paper/2311.10943/) (2023)
- [Multi-Model Least Squares-Based Recomputation Framework for Large Data Analysis](https://mlsanity.com/paper/2101.01271/) (2021)
- [Use of Deterministic Transforms to Design Weight Matrices of a Neural Network](https://mlsanity.com/paper/2110.03515/) (2021)
- [Uncertainty-Aware Ensemble Deep Randomized Neural Networks for Classification](https://mlsanity.com/paper/2608.10007/) (2026)
- [Residual-Guided Randomized Neural Networks](https://mlsanity.com/paper/2608.28267/) (2026)

## Abstract (arXiv)

In this paper, we propose a deep learning framework based on randomized neural network. In particular, inspired by the principles of Random Vector Functional Link (RVFL) network, we present a deep RVFL network (dRVFL) with stacked layers. The parameters of the hidden layers of the dRVFL are randomly generated within a suitable range and kept fixed while the output weights are computed using the closed form solution as in a standard RVFL network. We also propose an ensemble deep network (edRVFL) that can be regarded as a marriage of ensemble learning with deep learning. Unlike traditional ensembling approaches that require training several models independently from scratch, edRVFL is obtained by training a single dRVFL network once. Both dRVFL and edRVFL frameworks are generic and can be used with any RVFL variant. To illustrate this, we integrate the deep learning networks with a recently proposed sparse-pretrained RVFL (SP-RVFL). Extensive experiments on benchmark datasets from diverse domains show the superior performance of our proposed deep RVFL networks.