| Full text | |
| Author(s): |
Turesson, Hjalmar K.
;
Ribeiro, Sidarta
;
Pereira, Danillo R.
;
Papa, Joao P.
;
de Albuquerque, Victor Hugo C.
Total Authors: 5
|
| Document type: | Journal article |
| Source: | PLoS One; v. 11, n. 9 SEP 21 2016. |
| Web of Science Citations: | 11 |
| Abstract | |
Automatic classification of vocalization type could potentially become a useful tool for acoustic the monitoring of captive colonies of highly vocal primates. However, for classification to be useful in practice, a reliable algorithm that can be successfully trained on small datasets is necessary. In this work, we consider seven different classification algorithms with the goal of finding a robust classifier that can be successfully trained on small datasets. We found good classification performance (accuracy > 0.83 and F-1-score > 0.84) using the Optimum Path Forest classifier. Dataset and algorithms are made publicly available. (AU) | |
| FAPESP's process: | 14/16250-9 - On the parameter optimization in machine learning techniques: advances and paradigms |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat |
| Grantee: | Oswaldo Baffa Filho |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |
| FAPESP's process: | 15/50319-9 - Meta-heuristic-based optimization of probabilistic neural networks |
| Grantee: | João Paulo Papa |
| Support Opportunities: | Regular Research Grants |