Authors:
Jesus Aguilar–Ruiz
1
and
Matteo Fratini
2
Affiliations:
1
Pablo de Olavide University, ES–41013, Spain
;
2
Pharmagest, Italy
Keyword(s):
Neural Network, Ensemble, Random Forest, Very High Dimensionality.
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.