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The Taxonomy of Living Organisms Using Self-organizing Map

Received: 30 May 2020     Accepted: 15 June 2020     Published: 7 September 2020
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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.

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), 2020. Published by Science Publishing Group

Keywords

Self-organizing Map, Taxonomy, Unsupervised Neural Network, Classification

References
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[3] Science daily, https://www.sciencedaily.com/releases/2011/08/110823180459.htm.
[4] Michael A, Dennis P, Thomas M, Nicolas, Thierry B, Richard C, Thomas C, Michael D, Paul M. a higher level of classification of all living organisms. Integrated Taxonomic Information System.
[5] https://www.online-science.com/biology/modern-classification-of-living-organisms-kingdom-monera-and-protista.
[6] Wikipedia, Robert Whittaker (ecology), https://en.wikipedia.org/wiki/Robert_Whittaker_(ecologist).
[7] Wikibook, general biology/classification of living things/classification and domains of life. https://en.wikibooks.org/wiki/General_Biology/Classification_of_Living_Things/Classification_and_Domains_of_Life.
[8] Huston M. Biological Diversity: The Coexistence of Species; Cambridge University Press: Cambridge, UK, 1994.
[9] Dubravko M, Hrvatska E, Zagreb C. Brief Review of Self-Organizing Maps.
[10] Marie C, Madalina O, Fabrice R, Nathalie V (2016). Theoretical and applied aspect of Self-organizing map. https://hal.archives-ouvertes.fr/hal-01270701.
[11] Kohonen T (1990). The self-organizing map, Vol 78, issue 9.
[12] Kohonen T (2012). Essentials of Self-organizing map, National Liberary of Medicine. doi: 10.1016/j.neunet.2012.09.018.
[13] Umut A, Secil E (2012). An introduction to Self-organizing map: https://www.researchgate.net/publication/263084866.
[14] Merja O, Samuel K, Kohonen T (2003), Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum, Helsinki University of Technology, Neural Networks Research Centre, P. O. Box 5400, FIN-02015 HUT, FINLAND.
[15] Pollock R, Toby L, Michael W (2002). A Kohonen Self-Organizing Map for the functional classification of proteins based on one-dimensional sequence information, conference paper 2002. DOI: 10.1109/IJCNN.2002.1005467.
[16] Lars B, Bjorn G (2016). Self-Organizing Maps for Classification of a Multi-Labeled Corpu. Department of Computer and Information Science Norwegian University of Science and Technology.
[17] Herry-Derajad W, Saruni D (2019), clustering of earth quake data using Kohonen Self-organizing (SOM) algoeithm, DOI:10.21276/sb.2019.5.7.11.
[18] Faigl J (2016). An Application of Self-Organizing Map for Multirobot Multigoal Path Planning with Minmax Objective, vol Volume 2016, Article ID 2720630, 15 pages http://dx.doi.org/10.1155/2016/2720630.
[19] Marzieh M, Mahdi and Abdol Rassoul Z (2018). Using Self-Organizing Maps for Determination of Soil Fertility (Case Study: Shiraz Plain. Soil & Water Res., 13, 2018 (1): 11–17 doi: 10.17221/139/2016-SWR.
[20] Subana S, Sallis P, Buckeridge J. Self-organizing map for integrating data across multiple scales, e Auckland University of Technology, New Zealand, www.aut.ac.nz.
[21] Yuan-Chao L, Ming L and Xiao-Long W. Application of Self-Organizing maps in text clustering: A review. Doi: 10.5772/50618.
Cite This Article
  • 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

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    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

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    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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  • @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}
    }
    

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    T2  - American Journal of Artificial Intelligence
    JF  - American Journal of Artificial Intelligence
    JO  - American Journal of Artificial Intelligence
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    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.
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Author Information
  • Department of Computer Science, University of Lagos, Akoka, Yaba, Lagos, Nigeria

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