Paper Info
Title
Support vector machine under uncertainty: An application for hydroacoustic classification of fish-schools in Chile
Abstract
In this work we apply multi-class support vector machines (SVMs) and a multi-class stochastic SVM formulation to the classification of fish schools of three species: anchovy, common sardine, and Jack Mackerel, and we compare their performance. The data used come from acoustic measurements in southern-central Chile. These classifications were carried out by using a diver set of descriptors including morphology, bathymetry, energy, and space positions. In both type of formulations, the deterministic and the stochastic one, the strategy used to classify multi-class SVM consists in employing the criterion one-species-against-the-Rest. We thus provide an empirical way to adjust the parameters involved in the stochastic classifiers with the aim of improving its performance. When this procedure is applied to the classification of fish schools we obtain a classifier with a better performance than the deterministic classifier.
Year
DOI
Venue
2013
10.1016/j.eswa.2013.01.006
Expert Syst. Appl.
Keywords
Field
DocType
common sardine,multi-class svm,jack mackerel,hydroacoustic classification,deterministic classifier,stochastic classifier,criterion one-species-against-the-rest,acoustic measurement,multi-class support vector machine,fish school,better performance,support vector machines,species identification,multi class classification,second order cone programming
Second-order cone programming,Structured support vector machine,Data mining,Pattern recognition,Computer science,Support vector machine,Species identification,Artificial intelligence,Classifier (linguistics),Machine learning,Multiclass classification
Journal
Volume
Issue
ISSN
40
10
0957-4174
Citations 
PageRank 
References 
10
0.52
10
Authors
4
Name
Order
Citations
PageRank
Paul Bosch1100.86
Julio López212413.49
Héctor Ramírez3151.39
Hugo Robotham4100.86