Random Neural Network Ensemble for Very High Dimensional Datasets

Jesus Aguilar–Ruiz, Matteo Fratini

2024

Abstract

This paper introduces a machine learning method, Neural Network Ensemble (NNE), which combines ensemble learning principles with neural networks for classification tasks, particularly in the context of gene expression analysis. While the concept of weak learnability equalling strong learnability has been previously discussed, NNE’s unique features, such as addressing high dimensionality and blending Random Forest principles with experimental parameters, distinguish it within the ensemble landscape. The study evaluates NNE’s performance across five very high dimensional datasets, demonstrating competitive results compared to benchmark methods. Further analysis of the ensemble configuration, with respect to using variable–size neural networks units and guiding the selection of input variables would improve the classification performance of NNE–based architectures.

Paper Citation

in Harvard Style

Aguilar–Ruiz J. and Fratini M. (2024). Random Neural Network Ensemble for Very High Dimensional Datasets. In Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA; ISBN 978-989-758-707-8, SciTePress, pages 368-375. DOI: 10.5220/0012763800003756

in Bibtex Style

@conference{data24,
author={Jesus Aguilar–Ruiz and Matteo Fratini},
title={Random Neural Network Ensemble for Very High Dimensional Datasets},
booktitle={Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA},
year={2024},
pages={368-375},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012763800003756},
isbn={978-989-758-707-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Data Science, Technology and Applications - Volume 1: DATA
TI - Random Neural Network Ensemble for Very High Dimensional Datasets
SN - 978-989-758-707-8
AU - Aguilar–Ruiz J.
AU - Fratini M.
PY - 2024
SP - 368
EP - 375
DO - 10.5220/0012763800003756
PB - SciTePress