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Computer aided identification of biological specimens using self-organizing maps
Dissertation (MSc)--University of Pretoria, 2011.
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| Format: | Thesis |
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University of Pretoria
2013
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| _version_ | 1867613688358240256 |
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| access_status_str | Open Access |
| author2 | Engelbrecht, Andries P. |
| author_browse | Engelbrecht, Andries P. |
| author_facet | Engelbrecht, Andries P. |
| collection | Thesis |
| dc_rights_str_mv | © 2010 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MSc)--University of Pretoria, 2011. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/23116 |
| institution | University of Pretoria (South Africa) |
| last_indexed | 2026-06-10T12:40:07.413Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2013 |
| publishDateRange | 2013 |
| publishDateSort | 2013 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/23116 Computer aided identification of biological specimens using self-organizing maps Engelbrecht, Andries P. edean@saol.com Nicholas, A. Dean, Eileen J Tree identification Biological keys Biological identification Clustering and visualization Ann Artificial neural network Ai Artificial intelligence Botanical identification Acacia species Self-organizing map Unsupervised learning algorithm Som UCTD Dissertation (MSc)--University of Pretoria, 2011. For scientific or socio-economic reasons it is often necessary or desirable that biological material be identified. Given that there are an estimated 10 million living organisms on Earth, the identification of biological material can be problematic. Consequently the services of taxonomist specialists are often required. However, if such expertise is not readily available it is necessary to attempt an identification using an alternative method. Some of these alternative methods are unsatisfactory or can lead to a wrong identification. One of the most common problems encountered when identifying specimens is that important diagnostic features are often not easily observed, or may even be completely absent. A number of techniques can be used to try to overcome this problem, one of which, the Self Organizing Map (or SOM), is a particularly appealing technique because of its ability to handle missing data. This thesis explores the use of SOMs as a technique for the identification of indigenous trees of the Acacia species in KwaZulu-Natal, South Africa. The ability of the SOM technique to perform exploratory data analysis through data clustering is utilized and assessed, as is its usefulness for visualizing the results of the analysis of numerical, multivariate botanical data sets. The SOM’s ability to investigate, discover and interpret relationships within these data sets is examined, and the technique’s ability to identify tree species successfully is tested. These data sets are also tested using the C5 and CN2 classification techniques. Results from both these techniques are compared with the results obtained by using a SOM commercial package. These results indicate that the application of the SOM to the problem of biological identification could provide the start of the long-awaited breakthrough in computerized identification that biologists have eagerly been seeking. Computer Science unrestricted 2013-09-06T14:31:54Z 2011-05-10 2013-09-06T14:31:54Z 2010-04-25 2011-05-10 2011-01-12 Dissertation Dean, EJ 2010, Computer aided identification of biological specimens using self-organizing maps, MSc dissertation, University of Pretoria, Pretoria, viewed yymmdd < http://hdl.handle.net/2263/23116 > C11/44/ag http://hdl.handle.net/2263/23116 http://upetd.up.ac.za/thesis/available/etd-01122011-033543/ © 2010 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Tree identification Biological keys Biological identification Clustering and visualization Ann Artificial neural network Ai Artificial intelligence Botanical identification Acacia species Self-organizing map Unsupervised learning algorithm Som UCTD Computer aided identification of biological specimens using self-organizing maps |
| title | Computer aided identification of biological specimens using self-organizing maps |
| title_full | Computer aided identification of biological specimens using self-organizing maps |
| title_fullStr | Computer aided identification of biological specimens using self-organizing maps |
| title_full_unstemmed | Computer aided identification of biological specimens using self-organizing maps |
| title_short | Computer aided identification of biological specimens using self-organizing maps |
| title_sort | computer aided identification of biological specimens using self organizing maps |
| topic | Tree identification Biological keys Biological identification Clustering and visualization Ann Artificial neural network Ai Artificial intelligence Botanical identification Acacia species Self-organizing map Unsupervised learning algorithm Som UCTD |
| url | http://hdl.handle.net/2263/23116 http://upetd.up.ac.za/thesis/available/etd-01122011-033543/ |