期刊
ALGORITHMS
卷 2, 期 3, 页码 907-924出版社
MDPI
DOI: 10.3390/a2030907
关键词
ensembles; neural networks; support vector machines; echolocation calls; bats
资金
- New Zealand Foundation for Research, Science and Technology
- Royal Society (London)
- Strategic Environmental Research and Development Program (US Department of Defense)
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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