Show simple item record Kostadinova, Aneta Raga, Juan Antonio Montero, Francisco E. Perdiguero-Alonso, Diana Barrett, John 2009-05-21T12:00:33Z 2009-05-21T12:00:33Z 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 . DOI: 10.1016/j.ijpara.2008.04.007 en
dc.identifier.other PURE: 102743
dc.identifier.other PURE UUID: 876b9165-757c-4547-beb6-a0a6f5453b21
dc.identifier.other dspace: 2160/2322
dc.identifier.other DSpace_20121128.csv: row: 1692
dc.identifier.other IBERS: 0000017553
dc.identifier.other Ibers_20121112_1204.csv: row: 322
dc.identifier.other Scopus: 51249123328
dc.identifier.other PubMed: 18571175
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.rights 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 /dk/atira/pure/researchoutput/researchoutputtypes/contributiontojournal/article en
dc.contributor.institution Institute of Biological, Environmental and Rural Sciences en
dc.description.status Peer reviewed en

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