A bio-inspired weights and structure determination neural network for multiclass classification: Applications in occupational classification systems

Yu He; Xiaofan Dong; Theodore E. Simos; Spyridon D. Mourtas; Vasilios N. Katsikis; Dimitris Lagios; Panagiotis Zervas; Giannis Tzimas; Yu He; Xiaofan Dong; Theodore E. Simos; Spyridon D. Mourtas; Vasilios N. Katsikis; Dimitris Lagios; Panagiotis Zervas; Giannis Tzimas

doi:10.3934/math.2024119

AIMS Mathematics

2024, Volume 9, Issue 1: 2411-2434. doi: 10.3934/math.2024119

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A bio-inspired weights and structure determination neural network for multiclass classification: Applications in occupational classification systems

1.
School of Computer Science and Artificial Intelligence, Huanghuai University, Zhumadian 463000, China
2.
Henan Key Laboratory of Smart Lighting, Zhumadian 46300, China
3.
Henan International Joint Laboratory of Behavior Optimization Control for Smart Robots, Henan 463000, China
4.
Center for Applied Mathematics and Bioinformatics, Gulf University for Science and Technology, West Mishref, 32093 Kuwait
5.
Department of Medical Research, China Medical University Hospital, China Medical University, Taichung City 40402, Taiwan, China
6.
Laboratory of Inter-Disciplinary Problems of Energy Production, Ulyanovsk State Technical University, 32 Severny Venetz Street, 432027 Ulyanovsk, Russia
7.
Section of Mathematics, Dept. of Civil Engineering, Democritus Univ. of Thrace, Xanthi 67100, Greece
8.
Data Recovery Key Laboratory of Sichuan Province, Neijiang Normal Univ., Neijiang 641100, China
9.
Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
10.
Laboratory "Hybrid Methods of Modelling and Optimization in Complex Systems, " Siberian Federal University, Prosp. Svobodny 79, 660041 Krasnoyarsk, Russia
11.
Data and Media Laboratory, Department of Electrical and Computer Engineering, University of Peloponnese, Patras, Greece

Received: 13 September 2023 Revised: 12 December 2023 Accepted: 19 December 2023 Published: 25 December 2023
MSC : 68T10, 65F20, 91B40

Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts.
- neural networks,
- occupational classification,
- multiclass classification,
- machine learning,
- weights and structure determination,
- metaheuristics,
- beetle antennae search
Citation: Yu He, Xiaofan Dong, Theodore E. Simos, Spyridon D. Mourtas, Vasilios N. Katsikis, Dimitris Lagios, Panagiotis Zervas, Giannis Tzimas. A bio-inspired weights and structure determination neural network for multiclass classification: Applications in occupational classification systems[J]. AIMS Mathematics, 2024, 9(1): 2411-2434. doi: 10.3934/math.2024119

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Abstract

Undoubtedly, one of the most common machine learning challenges is multiclass classification. In light of this, a novel bio-inspired neural network (NN) has been developed to address multiclass classification-related issues. Given that weights and structure determination (WASD) NNs have been acknowledged to alleviate the disadvantages of conventional back-propagation NNs, such as slow training pace and trapping in a local minimum, we developed a bio-inspired WASD algorithm for multiclass classification problems (BWASDC) by using the metaheuristic beetle antennae search (BAS) algorithm to enhance the WASD algorithm's learning process. The BWASDC's effectiveness is then evaluated through applications in occupational classification systems. It is important to mention that systems of occupational classification serve as a fundamental indicator of occupational exposure. For this reason, they are highly significant in social science research. According to the findings of four occupational classification experiments, the BWASDC model outperformed some of the most modern classification models obtainable through MATLAB's classification learner app on all fronts.

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