Back to Search Start Over

A Hyperparameter Self-Evolving SHADE-Based Dendritic Neuron Model for Classification

Authors :
Haichuan Yang
Yuxin Zhang
Chaofeng Zhang
Wei Xia
Yifei Yang
Zhenwei Zhang
Source :
Axioms, Vol 12, Iss 11, p 1051 (2023)
Publication Year :
2023
Publisher :
MDPI AG, 2023.

Abstract

In recent years, artificial neural networks (ANNs), which are based on the foundational model established by McCulloch and Pitts in 1943, have been at the forefront of computational research. Despite their prominence, ANNs have encountered a number of challenges, including hyperparameter tuning and the need for vast datasets. It is because many strategies have predominantly focused on enhancing the depth and intricacy of these networks that the essence of the processing capabilities of individual neurons is occasionally overlooked. Consequently, a model emphasizing a biologically accurate dendritic neuron model (DNM) that mirrors the spatio-temporal features of real neurons was introduced. However, while the DNM shows outstanding performance in classification tasks, it struggles with complexities in parameter adjustments. In this study, we introduced the hyperparameters of the DNM into an evolutionary algorithm, thereby transforming the method of setting DNM’s hyperparameters from the previous manual adjustments to adaptive adjustments as the algorithm iterates. The newly proposed framework, represents a neuron that evolves alongside the iterations, thus simplifying the parameter-tuning process. Comparative evaluation on benchmark classification datasets from the UCI Machine Learning Repository indicates that our minor enhancements lead to significant improvements in the performance of DNM, surpassing other leading-edge algorithms in terms of both accuracy and efficiency. In addition, we also analyzed the iterative process using complex networks, and the results indicated that the information interaction during the iteration and evolution of the DNM follows a power-law distribution. With this finding, some insights could be provided for the study of neuron model training.

Details

Language :
English
ISSN :
20751680
Volume :
12
Issue :
11
Database :
Directory of Open Access Journals
Journal :
Axioms
Publication Type :
Academic Journal
Accession number :
edsdoj.6350793483264eadb82964dbbdbf40d0
Document Type :
article
Full Text :
https://doi.org/10.3390/axioms12111051