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Morphometric shape analysis using learning vector quantization neural networks: an example distinguishing two microtine vole species
Umeå University, Faculty of Science and Technology, Department of Ecology and Environmental Sciences.
2011 (English)In: Annales Zoologici Fennici, ISSN 0003-455X, E-ISSN 1797-2450, Vol. 48, no 6, 359-364 p.Article in journal (Refereed) Published
Abstract [en]

Closely related species may be very difficult to distinguish morphologically, yet sometimes morphology is the only reasonable possibility for taxonomic classification. Here we present learning-vector-quantization artificial neural networks as a powerful tool to classify specimens on the basis of geometric morphometric shape measurements. As an example, we trained a neural network to distinguish between field and root voles from Procrustes transformed landmark coordinates on the dorsal side of the skull, which is so similar in these two species that the human eye cannot make this distinction. Properly trained neural networks misclassified only 3% of specimens. Therefore, we conclude that the capacity of learning vector quantization neural networks to analyse spatial coordinates is a powerful tool among the range of pattern recognition procedures that is available to employ the information content of geometric morphometrics.

Place, publisher, year, edition, pages
2011. Vol. 48, no 6, 359-364 p.
National Category
Zoology
Identifiers
URN: urn:nbn:se:umu:diva-51651ISI: 000298974700003OAI: oai:DiVA.org:umu-51651DiVA: diva2:488695
Available from: 2012-02-02 Created: 2012-01-31 Last updated: 2017-12-08Bibliographically approved

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Bokma, Folmer

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