A Weights Direct Determination Neural Network for International Standard Classification of Occupations

Dimitris Lagios, Spyridon D. Mourtas (), Panagiotis Zervas and Giannis Tzimas
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Dimitris Lagios: Data and Media Laboratory, Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patras, Greece
Spyridon D. Mourtas: Department of Economics, Mathematics-Informatics and Statistics-Econometrics, National and Kapodistrian University of Athens, Sofokleous 1 Street, 10559 Athens, Greece
Panagiotis Zervas: Data and Media Laboratory, Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patras, Greece
Giannis Tzimas: Data and Media Laboratory, Department of Electrical and Computer Engineering, University of Peloponnese, 26334 Patras, Greece

Mathematics, 2023, vol. 11, issue 3, 1-14

Abstract: Multiclass classification is one of the most popular machine learning tasks. The main focus of this paper is to classify occupations according to the International Standard Classification of Occupations (ISCO) using a weights and structure determination (WASD)-based neural network. In general, WASD-trained neural networks are known to overcome the drawbacks of conventional back-propagation trained neural networks, such as slow training speed and local minimum. However, WASD-based neural networks have not yet been applied to address the challenges of multiclass classification. As a result, a novel WASD for multiclass classification (WASDMC)-based neural network is introduced in this paper. When applied to two publicly accessible ISCO datasets, the WASDMC-based neural network displayed superior performance across all measures, compared to some of the best-performing classification models that the MATLAB classification learner app has to offer.

Keywords: neural networks; weights and structure determination; multiclass classification; international standard classification of occupations; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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