Classification of echolocation calls from 14 species of bat by Support Vector Machines and Ensembles of Neural Networks
Redgwell, RD, Szewczak, J, Jones, G, & Parsons, Stuart (2009) Classification of echolocation calls from 14 species of bat by Support Vector Machines and Ensembles of Neural Networks. Algorithms, 2(3), pp. 907-924.
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.
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|Item Type:||Journal Article|
|Copyright Owner:||Copyright 2009 The authors; licensee Molecular Diversity Preservation International, Basel, Switzerland.|
|Copyright Statement:||This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).|
|Deposited On:||21 Jan 2015 02:55|
|Last Modified:||22 Jan 2015 21:31|
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