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dc.contributor.authorFernandes, Jose A.-
dc.contributor.authorIrigoien, Xabier-
dc.contributor.authorGoikoetxea, Nerea-
dc.contributor.authorLozano, Jose A.-
dc.contributor.authorInza, Inaki-
dc.contributor.authorPerez, Aritz-
dc.contributor.authorBode, Antonio-
dc.date.accessioned2019-05-31T08:50:41Z-
dc.date.available2019-05-31T08:50:41Z-
dc.date.issued2010-
dc.identifierISI:000273628800021-
dc.identifier.citationECOLOGICAL MODELLING, 2010, 221, 338-352-
dc.identifier.issn0304-3800-
dc.identifier.urihttp://dspace.azti.es/handle/24689/748-
dc.description.abstractImproving our ability to predict recruitment is a key element in fisheries management. However, the interactions between population dynamics and different environmental factors are complex and often non-linear, making it difficult to produce robust predictions. `Machine-learning' techniques (in particular, supervised classification methods) have been proposed as useful tools, to overcome such difficulties. In this study, a methodology is proposed to build a robust classifier for fish recruitment prediction with sparse and noisy data. The methodology consists of 4 steps: (1) a semi-automated recruitment discretization method; (2) supervised discretization of predictors; (3) multivariate and non-redundant predictors selection: (4) learning a probabilistic classifier. in terms of fisheries management, the classifier estimated performance has important consequences and, to be useful, the manager needs to know the risk that is being taken when using this number. Probabilistic classifiers such as `naive Bayes', have the advantage that, in addition to the predictions, estimate also the probability of each possible outcome. Anchovy (Engraulis encrasicolus) and hake (Merluccius merluccius) recruitments are used as application examples. `Two-intervals' recruitment discretization accomplishes 70\% accuracies and Brier scores of around 0.10, for both anchovy and hake recruitment. In comparison, `three-intervals' recruitment discretization accomplishes 50\% accuracies; and Brier scores of around 0.25 for anchovy and 0.30 for hake recruitment. These statistics are the result of validating not only the classifier, but also the previous steps, as a whole methodology. (C) 2009 Elsevier B.V. All rights reserved.-
dc.language.isoeng-
dc.publisherELSEVIER SCIENCE BV-
dc.subjectSupervised classification-
dc.subjectEcological modelling-
dc.subjectFish recruitment-
dc.subjectDiscretization-
dc.subjectFeature selection-
dc.subjectClimate-
dc.subjectAnchovy-
dc.subjectHake-
dc.subjectANCHOVY ENGRAULIS-ENCRASICOLUS-
dc.subjectBAYESIAN NETWORKS-
dc.subjectDISTRIBUTION ALGORITHMS-
dc.subjectBISCAY ANCHOVY-
dc.subjectCLIMATE-CHANGE-
dc.subjectBAY-
dc.subjectENVIRONMENT-
dc.subjectMODEL-
dc.subjectSEA-
dc.subjectDISCRETIZATION-
dc.titleFish recruitment prediction, using robust supervised classification methods-
dc.typeArticle-
dc.identifier.journalECOLOGICAL MODELLING-
dc.format.page338-352-
dc.format.volume221-
dc.contributor.funderFundacion Centros Tecnologicos Inaki Goenaga-
dc.contributor.funderDepartment of Agriculture, Fisheries and Food of the Basque Country Government-
dc.contributor.funderBasque Government [TIN2008-06815-C02-01]-
dc.contributor.funderSpanish Ministry of Education and Science [2010-CSD2007-00018]-
dc.contributor.funderCOMBIOMED network in computational biomedicine (Carlos III Health Institute)-
dc.contributor.funderEU [212085]-
dc.identifier.doi10.1016/j.ecolmodel.2009.09.020-
Aparece en las tipos de publicación: Artículos científicos



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