The Self Organizing Map (SOM) is an unsupervised network algorithm that projects high dimensional data into low dimensional maps. The projection preserves the topology of the data so that similar data items are mapped to nearby locations on the map. The algorithm has been so popular because of its application in Computer Science and other areas; it has been applied in speech recognition, pattern identification, control engineering, earthquake detection et al. This research aimed to apply the SOM in the taxonomy of living organisms using 46 attributes. 68 animals from 6 phyla were considered and 46 attributes were used detailing their physical features, physiological features, evolution, adaptation, habitat et al. The features extracted were converted to 0s and 1s for the SOM algorithm to process. The result shows 96.569% accuracy of the SOM’s classification but better accuracy can be obtained if the SOM had processed the data for about 1000 iterations. This research revealed that SOM is a veritable tool or algorithm that can be used to classify living organisms. This research will help taxonomists, biologists and students who spend much time in classifying living organism and it will be of help to researchers who want to explore the SOM algorithm as a solution to taxonomy of living organisms. The SOM will ease taxonomy and will help to minimize the stress and time involved in classifying thousands of living organisms.
| Published in | American Journal of Artificial Intelligence (Volume 4, Issue 2) |
| DOI | 10.11648/j.ajai.20200402.12 |
| Page(s) | 50-61 |
| 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), 2024. Published by Science Publishing Group |
Self-organizing Map, Taxonomy, Unsupervised Neural Network, Classification
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APA Style
Adebayo Rotimi Philip. (2020). The Taxonomy of Living Organisms Using Self-organizing Map. American Journal of Artificial Intelligence, 4(2), 50-61. https://doi.org/10.11648/j.ajai.20200402.12
ACS Style
Adebayo Rotimi Philip. The Taxonomy of Living Organisms Using Self-organizing Map. Am. J. Artif. Intell. 2020, 4(2), 50-61. doi: 10.11648/j.ajai.20200402.12
AMA Style
Adebayo Rotimi Philip. The Taxonomy of Living Organisms Using Self-organizing Map. Am J Artif Intell. 2020;4(2):50-61. doi: 10.11648/j.ajai.20200402.12
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Download@article{10.11648/j.ajai.20200402.12,
author = {Adebayo Rotimi Philip},
title = {The Taxonomy of Living Organisms Using Self-organizing Map},
journal = {American Journal of Artificial Intelligence},
volume = {4},
number = {2},
pages = {50-61},
doi = {10.11648/j.ajai.20200402.12},
url = {https://doi.org/10.11648/j.ajai.20200402.12},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajai.20200402.12},
abstract = {The Self Organizing Map (SOM) is an unsupervised network algorithm that projects high dimensional data into low dimensional maps. The projection preserves the topology of the data so that similar data items are mapped to nearby locations on the map. The algorithm has been so popular because of its application in Computer Science and other areas; it has been applied in speech recognition, pattern identification, control engineering, earthquake detection et al. This research aimed to apply the SOM in the taxonomy of living organisms using 46 attributes. 68 animals from 6 phyla were considered and 46 attributes were used detailing their physical features, physiological features, evolution, adaptation, habitat et al. The features extracted were converted to 0s and 1s for the SOM algorithm to process. The result shows 96.569% accuracy of the SOM’s classification but better accuracy can be obtained if the SOM had processed the data for about 1000 iterations. This research revealed that SOM is a veritable tool or algorithm that can be used to classify living organisms. This research will help taxonomists, biologists and students who spend much time in classifying living organism and it will be of help to researchers who want to explore the SOM algorithm as a solution to taxonomy of living organisms. The SOM will ease taxonomy and will help to minimize the stress and time involved in classifying thousands of living organisms.},
year = {2020}
}
TY - JOUR T1 - The Taxonomy of Living Organisms Using Self-organizing Map AU - Adebayo Rotimi Philip Y1 - 2020/09/07 PY - 2020 N1 - https://doi.org/10.11648/j.ajai.20200402.12 DO - 10.11648/j.ajai.20200402.12 T2 - American Journal of Artificial Intelligence JF - American Journal of Artificial Intelligence JO - American Journal of Artificial Intelligence SP - 50 EP - 61 PB - Science Publishing Group SN - 2639-9733 UR - https://doi.org/10.11648/j.ajai.20200402.12 AB - The Self Organizing Map (SOM) is an unsupervised network algorithm that projects high dimensional data into low dimensional maps. The projection preserves the topology of the data so that similar data items are mapped to nearby locations on the map. The algorithm has been so popular because of its application in Computer Science and other areas; it has been applied in speech recognition, pattern identification, control engineering, earthquake detection et al. This research aimed to apply the SOM in the taxonomy of living organisms using 46 attributes. 68 animals from 6 phyla were considered and 46 attributes were used detailing their physical features, physiological features, evolution, adaptation, habitat et al. The features extracted were converted to 0s and 1s for the SOM algorithm to process. The result shows 96.569% accuracy of the SOM’s classification but better accuracy can be obtained if the SOM had processed the data for about 1000 iterations. This research revealed that SOM is a veritable tool or algorithm that can be used to classify living organisms. This research will help taxonomists, biologists and students who spend much time in classifying living organism and it will be of help to researchers who want to explore the SOM algorithm as a solution to taxonomy of living organisms. The SOM will ease taxonomy and will help to minimize the stress and time involved in classifying thousands of living organisms. VL - 4 IS - 2 ER -
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