Application of classifiers: Support vector machines, artificial neural networks and classification trees to identify acoustic schools

Abstract

The purpose of this study was to compare the results of the classification of the pelagic fish species, the common sardine, anchovy, and jack mackerel with classification trees (CART), Support Vector Machine (SVM) and artificial neural network (multilayer perceptron, MLP), using mono-frequency acoustic data in southern-central Chile. The classifiers had similar performances, those of the MLP and SVM being the same, while t hat of CART was the lowest. The separation of anchovy and common sardine is considered acceptable with all methods, 90.8% for anchovy and between 87.4% (CART) and 90.3% (MLP) for sardine. These performances were higher than that for the jack mackerel, 77.8% (CART), 81.5% (MLP) and 85.2% (SVM). There is concordance on the groups of descriptors (bathymetric and positional) considered as effective for classification in all methods, but the importance of the descriptors presented by each method is not fully concordant. The energetic and morphological descriptor had low incidence. We recommend trying many classifiers to identify acoustic schools as a good practice.