Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes

Adebayo Rotimi Philip

doi:doi:10.11648/j.ijiis.20231201.12

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Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes

Adebayo Rotimi Philip

Published in International Journal of Intelligent Information Systems (Volume 12, Issue 1)

Received: 18 February 2023 Accepted: 16 March 2023 Published: 28 March 2023

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Abstract

Many organizations such as World Bank, UN, Wikipedia and others have tried to classify countries as under-developed, developing, developed and highly developed countries based on certain criteria but these criteria aren’t robust enough. In most cases, they used one to three criteria. This research classified 195 countries using 32 attributes (features/ criteria) with the self-organizing map (SOM) algorithm. This is a robust classification because 32 features are considered for the classification. SOM is an unsupervised learning algorithm which reduces high dimensional data to 2 dimensions. The SOM classifies the 195 countries into 5 categories, implying that it is possible to classify countries with SOM algorithm. There is no benchmark to measure the accuracy of the SOM algorithm because most classifications are based on at most three criteria which are not robust enough, but comparing the results of the SOM algorithm with these weak classifications still show the flawlessness of the SOM algorithm. This research will help scientist, students, lecturers, teachers, organizations and countries to have a robust knowledge about the state of their countries from an unbiased position and will also help organizations and countries to make concrete decisions about business establishment in viable places all over the world. The key limitation is the reliability of the data and the number of attributes, which could be increased in future researches for better results.

Published in	International Journal of Intelligent Information Systems (Volume 12, Issue 1)
DOI	10.11648/j.ijiis.20231201.12
Page(s)	10-25
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2023. Published by Science Publishing Group

Keywords

Economic Classification, Self-Organizing Map, Highly Developed, Developed Developing, Under-Developed Countries, Clustering, Features, Attributes

References

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APA Style

Adebayo Rotimi Philip. (2023). Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes. International Journal of Intelligent Information Systems, 12(1), 10-25. https://doi.org/10.11648/j.ijiis.20231201.12

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ACS Style

Adebayo Rotimi Philip. Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes. Int. J. Intell. Inf. Syst. 2023, 12(1), 10-25. doi: 10.11648/j.ijiis.20231201.12

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AMA Style

Adebayo Rotimi Philip. Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes. Int J Intell Inf Syst. 2023;12(1):10-25. doi: 10.11648/j.ijiis.20231201.12

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@article{10.11648/j.ijiis.20231201.12,
  author = {Adebayo Rotimi Philip},
  title = {Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes},
  journal = {International Journal of Intelligent Information Systems},
  volume = {12},
  number = {1},
  pages = {10-25},
  doi = {10.11648/j.ijiis.20231201.12},
  url = {https://doi.org/10.11648/j.ijiis.20231201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20231201.12},
  abstract = {Many organizations such as World Bank, UN, Wikipedia and others have tried to classify countries as under-developed, developing, developed and highly developed countries based on certain criteria but these criteria aren’t robust enough. In most cases, they used one to three criteria. This research classified 195 countries using 32 attributes (features/ criteria) with the self-organizing map (SOM) algorithm. This is a robust classification because 32 features are considered for the classification. SOM is an unsupervised learning algorithm which reduces high dimensional data to 2 dimensions. The SOM classifies the 195 countries into 5 categories, implying that it is possible to classify countries with SOM algorithm. There is no benchmark to measure the accuracy of the SOM algorithm because most classifications are based on at most three criteria which are not robust enough, but comparing the results of the SOM algorithm with these weak classifications still show the flawlessness of the SOM algorithm. This research will help scientist, students, lecturers, teachers, organizations and countries to have a robust knowledge about the state of their countries from an unbiased position and will also help organizations and countries to make concrete decisions about business establishment in viable places all over the world. The key limitation is the reliability of the data and the number of attributes, which could be increased in future researches for better results.},
 year = {2023}
}

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TY  - JOUR
T1  - Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes
AU  - Adebayo Rotimi Philip
Y1  - 2023/03/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijiis.20231201.12
DO  - 10.11648/j.ijiis.20231201.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 10
EP  - 25
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20231201.12
AB  - Many organizations such as World Bank, UN, Wikipedia and others have tried to classify countries as under-developed, developing, developed and highly developed countries based on certain criteria but these criteria aren’t robust enough. In most cases, they used one to three criteria. This research classified 195 countries using 32 attributes (features/ criteria) with the self-organizing map (SOM) algorithm. This is a robust classification because 32 features are considered for the classification. SOM is an unsupervised learning algorithm which reduces high dimensional data to 2 dimensions. The SOM classifies the 195 countries into 5 categories, implying that it is possible to classify countries with SOM algorithm. There is no benchmark to measure the accuracy of the SOM algorithm because most classifications are based on at most three criteria which are not robust enough, but comparing the results of the SOM algorithm with these weak classifications still show the flawlessness of the SOM algorithm. This research will help scientist, students, lecturers, teachers, organizations and countries to have a robust knowledge about the state of their countries from an unbiased position and will also help organizations and countries to make concrete decisions about business establishment in viable places all over the world. The key limitation is the reliability of the data and the number of attributes, which could be increased in future researches for better results.
VL  - 12
IS  - 1
ER  -

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Author Information

Adebayo Rotimi Philip

Department of Computer Science, University of Lagos, Akoka, Nigeria

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Plain Text BibTeX RIS

APA Style

Adebayo Rotimi Philip. (2023). Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes. International Journal of Intelligent Information Systems, 12(1), 10-25. https://doi.org/10.11648/j.ijiis.20231201.12

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ACS Style

Adebayo Rotimi Philip. Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes. Int. J. Intell. Inf. Syst. 2023, 12(1), 10-25. doi: 10.11648/j.ijiis.20231201.12

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AMA Style

Adebayo Rotimi Philip. Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes. Int J Intell Inf Syst. 2023;12(1):10-25. doi: 10.11648/j.ijiis.20231201.12

Copy | Download

@article{10.11648/j.ijiis.20231201.12,
  author = {Adebayo Rotimi Philip},
  title = {Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes},
  journal = {International Journal of Intelligent Information Systems},
  volume = {12},
  number = {1},
  pages = {10-25},
  doi = {10.11648/j.ijiis.20231201.12},
  url = {https://doi.org/10.11648/j.ijiis.20231201.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20231201.12},
  abstract = {Many organizations such as World Bank, UN, Wikipedia and others have tried to classify countries as under-developed, developing, developed and highly developed countries based on certain criteria but these criteria aren’t robust enough. In most cases, they used one to three criteria. This research classified 195 countries using 32 attributes (features/ criteria) with the self-organizing map (SOM) algorithm. This is a robust classification because 32 features are considered for the classification. SOM is an unsupervised learning algorithm which reduces high dimensional data to 2 dimensions. The SOM classifies the 195 countries into 5 categories, implying that it is possible to classify countries with SOM algorithm. There is no benchmark to measure the accuracy of the SOM algorithm because most classifications are based on at most three criteria which are not robust enough, but comparing the results of the SOM algorithm with these weak classifications still show the flawlessness of the SOM algorithm. This research will help scientist, students, lecturers, teachers, organizations and countries to have a robust knowledge about the state of their countries from an unbiased position and will also help organizations and countries to make concrete decisions about business establishment in viable places all over the world. The key limitation is the reliability of the data and the number of attributes, which could be increased in future researches for better results.},
 year = {2023}
}

Copy | Download

TY  - JOUR
T1  - Applying the Self-Organizing Map in the Classification of 195 Countries Using 32 Attributes
AU  - Adebayo Rotimi Philip
Y1  - 2023/03/28
PY  - 2023
N1  - https://doi.org/10.11648/j.ijiis.20231201.12
DO  - 10.11648/j.ijiis.20231201.12
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 10
EP  - 25
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20231201.12
AB  - Many organizations such as World Bank, UN, Wikipedia and others have tried to classify countries as under-developed, developing, developed and highly developed countries based on certain criteria but these criteria aren’t robust enough. In most cases, they used one to three criteria. This research classified 195 countries using 32 attributes (features/ criteria) with the self-organizing map (SOM) algorithm. This is a robust classification because 32 features are considered for the classification. SOM is an unsupervised learning algorithm which reduces high dimensional data to 2 dimensions. The SOM classifies the 195 countries into 5 categories, implying that it is possible to classify countries with SOM algorithm. There is no benchmark to measure the accuracy of the SOM algorithm because most classifications are based on at most three criteria which are not robust enough, but comparing the results of the SOM algorithm with these weak classifications still show the flawlessness of the SOM algorithm. This research will help scientist, students, lecturers, teachers, organizations and countries to have a robust knowledge about the state of their countries from an unbiased position and will also help organizations and countries to make concrete decisions about business establishment in viable places all over the world. The key limitation is the reliability of the data and the number of attributes, which could be increased in future researches for better results.
VL  - 12
IS  - 1
ER  -

Copy | Download