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Email the author (Login required) Acoustic classification of anchovy (Engraulis ringens) and sardine (Strangomera bentincki) using support vector machines in central-southern Chile: effect of parameter calibration on the confusion matrix
DOI: https://doi.org/10.3856/vol40-issue1-fulltext-9
Abstract
The support vector machines (SVM) method was used to classify the anchovy (Engraulis ringens) and common sardine (Strangomera bentincki) species detected in south–central Chile by means of acoustic equipment. For this, descriptors of fish schools (morphology, bathymetry, energy, spatial position) extracted from ecograms were used. In order to obtain precise classifications using this methodology, it was necessary to optimize the parameters Gaussian-Kernel γ and penalty term C by analyzing the effect of the calibration on the confusion matrices resulting from the classification of the species under study. The SVM method correctly classified 95.3% of anchovy and sardine schools. The optimal parameters of the GaussianKernel γ and penalty C obtained with the proposed methodology were γ = 450 and C = 0.95. These parameters have an important influence over the confusion matrix and the final classifications percentages, suggesting the development of experimental protocols for calibrating these parameters in future applications of this methodology. In all the confusion matrices, the common sardine showed the lowest classification error. The bottom depth was the descriptor that was most sensitive to the SVM, followed by school-shore distance.
