Abstract
The neuronal approach has interested a large number of researchers for analysis and in various fields. In this article, we use Kohonen Self-Organizing Map (SOM) which is an unsupervised neural network algorithm that projects high-dimensional data to predict dimension classification of the gender inequality index. This study covers 145 countries, demonstrates the relevance of the neural approach in this field of research. It was possible to determine an “optimal map” which involves a classification of countries and a view of the situation of inequalities in order to draw several relevant conclusions. The classification was carried out by the level of evolution of each dimension of the gender inequality index. Each group of countries classified in the same cell implies that these countries have suffered similar effects for the inequality indicators or that they have applied the same strategy to fight inequality. Grouping countries by zone shows, on the one hand, that countries with high inequalities are characterized by a strong correlation between dimensions. Second, African and Asian countries have the greatest deficit in education, health and the labor market.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
More From: International Journal on Data Science and Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.