Random forests, a novel approach for discrimination of fish populations using parasites as biological tags

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dc.contributor.author Kostadinova, Aneta
dc.contributor.author Raga, Juan Antonio
dc.contributor.author Montero, Francisco E.
dc.contributor.author Perdiguero-Alonso, Diana
dc.contributor.author Barrett, John
dc.date.accessioned 2009-05-21T12:00:33Z
dc.date.available 2009-05-21T12:00:33Z
dc.date.issued 2008
dc.identifier.citation Kostadinova , A , Raga , J A , Montero , F E , Perdiguero-Alonso , D & Barrett , J 2008 , ' Random forests, a novel approach for discrimination of fish populations using parasites as biological tags ' International Journal for Parasitology , vol 38 , pp. 1425-1434 . en
dc.identifier.other PURE: 102743
dc.identifier.other dspace: 2160/2322
dc.identifier.uri http://hdl.handle.net/2160/2322
dc.description Perdiguero-Alonso, D., Montero, F. E., Kostadinova, A., Raga, J. A., Barrett, J. (2008). Random forests, a novel approach for discrimination of fish populations using parasites as biological tags. International Journal for Parasitology, 38, pp. 1425-1434 Keywords: Random forests; Classification algorithms; Fish population discrimination; Parasite communities; Atlantic cod; Gadus morhua; North East Atlantic On file IMPF: 03.75 RONO: 00 en
dc.description.abstract Due to the complexity of host–parasite relationships, discrimination between fish populations using parasites as biological tags is difficult. This study introduces, to our knowledge for the first time, random forests (RF) as a new modelling technique in the application of parasite community data as biological markers for population assignment of fish. This novel approach is applied to a dataset with a complex structure comprising 763 parasite infracommunities in population samples of Atlantic cod, Gadus morhua, from the spawning/feeding areas in five regions in the North East Atlantic (Baltic, Celtic, Irish and North seas and Icelandic waters). The learning behaviour of RF is evaluated in comparison with two other algorithms applied to class assignment problems, the linear discriminant function analysis (LDA) and artificial neural networks (ANN). The three algorithms are used to develop predictive models applying three cross-validation procedures in a series of experiments (252 models in total). The comparative approach to RF, LDA and ANN algorithms applied to the same datasets demonstrates the competitive potential of RF for developing predictive models since RF exhibited better accuracy of prediction and outperformed LDA and ANN in the assignment of fish to their regions of sampling using parasite community data. The comparative analyses and the validation experiment with a ‘blind’ sample confirmed that RF models performed more effectively with a large and diverse training set and a large number of variables. The discrimination results obtained for a migratory fish species with largely overlapping parasite communities reflects the high potential of RF for developing predictive models using data that are both complex and noisy, and indicates that it is a promising tool for parasite tag studies. Our results suggest that parasite community data can be used successfully to discriminate individual cod from the five different regions of the North East Atlantic studied using RF. en
dc.format.extent 10 en
dc.language.iso eng
dc.relation.ispartof International Journal for Parasitology en
dc.subject Random forests en
dc.subject Classification algorithms en
dc.subject Fish population discrimination en
dc.subject Parasite communities en
dc.subject Atlantic cod en
dc.subject Gadus morhua en
dc.subject North East Atlantic en
dc.title Random forests, a novel approach for discrimination of fish populations using parasites as biological tags en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1016/j.ijpara.2008.04.007
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en


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