Algorithms 2009, 2(3), 907-924; doi:10.3390/a2030907
Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks
1
School of Biological Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand
2
Department of Biological Sciences, Humboldt State University, 1 Harpst St., Arcata, California 95521, USA
3
School of Biological Sciences, University of Bristol, Woodland Road, Bristol BS8 1UG, UK
*
Author to whom correspondence should be addressed.
Received: 16 June 2009 / Accepted: 1 July 2009 / Published: 9 July 2009
(This article belongs to the Special Issue Neural Networks and Sensors)
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Abstract
Calls from 14 species of bat were classified to genus and species using discriminant function analysis (DFA), support vector machines (SVM) and ensembles of neural networks (ENN). Both SVMs and ENNs outperformed DFA for every species while ENNs (mean identification rate – 97%) consistently outperformed SVMs (mean identification rate – 87%). Correct classification rates produced by the ENNs varied from 91% to 100%; calls from six species were correctly identified with 100% accuracy. Calls from the five species of Myotis, a genus whose species are considered difficult to distinguish acoustically, had correct identification rates that varied from 91 – 100%. Five parameters were most important for classifying calls correctly while seven others contributed little to classification performance. View Full-TextKeywords:
ensembles; neural networks; support vector machines; echolocation calls; bats
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).
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MDPI and ACS Style
Redgwell, R.D.; Szewczak, J.M.; Jones, G.; Parsons, S. Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks. Algorithms 2009, 2, 907-924.
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