Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks

Robert Redgwell; Joseph Szewczak; Gareth Jones; Stuart Parsons

doi:10.3390/a2030907

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Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks

Journal article

Open access

Peer reviewed

Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks

Robert Redgwell, Joseph Szewczak, Gareth Jones and Stuart Parsons

Algorithms, Vol.2(3), pp.907-924

2009

DOI: https://doi.org/10.3390/a2030907

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Abstract

Neural networks

ensembles

support vector machines

echolocation calls

bats

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.

Details

Title: Classification of Echolocation Calls from 14 Species of Bat by Support Vector Machines and Ensembles of Neural Networks
Authors: Robert Redgwell (Author) - University of Auckland
Joseph Szewczak (Author) - Cal Poly Humboldt
Gareth Jones (Author) - University of Bristol
Stuart Parsons (Author) - University of Auckland
Publication details: Algorithms, Vol.2(3), pp.907-924
Publisher: MDPI AG
Date published: 2009
DOI: 10.3390/a2030907
ISSN: 1999-4893
Copyright note: © 2009 by the authors; licensee Molecular Diversity Preservation International, Basel, Switzerland. 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/).
Organisation Unit: University of the Sunshine Coast, Queensland; School of Science, Technology and Engineering
Language: English
Record Identifier: 99576208202621
Output Type: Journal article

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Web Of Science research areas: Computer Science, Artificial Intelligence; Computer Science, Theory & Methods

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