http://www.cnr.it/ontology/cnr/individuo/prodotto/ID6844

IPEZ: an expert system for the Taxonomic identification of Fishes based on machine learning techniques (Articolo in rivista)

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IPEZ: an expert system for the Taxonomic identification of Fishes based on machine learning techniques (Articolo in rivista) (literal)

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C. Guisande (a); A. Manjarrés-Hernández (b); P. Pelayo-Villamil (c); C. Granado-Lorencio (d); I. Riveiro (a); A. Acu~na (a); E. Prieto-Piraquive(d); E. Janeiro (e); J.M. Matías (e); C. Patti (f); B. Patti (f); S. Mazzola (f); S. Jiménez (g); V. Duque (g); F. Salmerón (h) (2010)
IPEZ: an expert system for the Taxonomic identification of Fishes based on machine learning techniques
in Fisheries research; Academic Press Elsevier, Amsterdam (Paesi Bassi)
(literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori

C. Guisande (a); A. Manjarrés-Hernández (b); P. Pelayo-Villamil (c); C. Granado-Lorencio (d); I. Riveiro (a); A. Acu~na (a); E. Prieto-Piraquive(d); E. Janeiro (e); J.M. Matías (e); C. Patti (f); B. Patti (f); S. Mazzola (f); S. Jiménez (g); V. Duque (g); F. Salmerón (h) (literal)

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a Facultad de Ciencias, Universidad de Vigo, Campus Lagoas-Marcosende, 36200 Vigo, Spain b Instituto Amazónico de Investigaciones-IMANI, Universidad Nacional de Colombia, km 2 vía Tarapacá, Leticia, Colombia c Grupo de Ictiología, Universidad de Antioquia, A.A. 1226, Medellín, Colombia d Facultad de Biología, Universidad de Sevilla, 41012 Sevilla, Spain e Departamento de lngeniería de los Recursos Naturales y Medio Ambiente, Universidad de Vigo, Campus Lagoas-Marcosende, 36310 Vigo, Spain f Istituto per l'Ambiente Marino Costiero, Consiglio Nazionale delle Ricerche, Sede diMazara del Vallo, via L. Vaccara 61, 91026 Mazara del Vallo, TP, Italy g Instituto Espa~nol de Oceanografía, Centro Oceanográfico Canarias, Carretera de San Andrés, s/n, 38120 Tenerife, Spain h Instituto Espa~nol de Oceanografía, Centro Oceanográfico de Málaga, Puerto Pesquero, s/n, 29640 Fuengirola, Spain (literal)

Titolo

IPEZ: an expert system for the Taxonomic identification of Fishes based on machine learning techniques (literal)

Abstract

The taxonomic identification of fishes is a time-consuming process for those who are not specialists and, therefore, are more liable to make mistakes. We measured morphometric characters in more than 8900 individuals belonging to 6 classes, 43 orders, 192 families, 510 genera and 847 marine and freshwater species. The aim was to determine if the taxonomic identification of juvenile and adult fishes is possible using these measurements. We developed the expert system IPez, which is based on machine learning techniques, and found that, when the number of individuals measured of a species and, hence, included in the database of IPez, is higher than approximately 15 individuals, IPez identifies correctly 100% of new individuals of this species that are not included into the database. Moreover, besides helping in the taxonomic identification of fish, this software allows the determining of the main morphometric characters that have promoted or are promoting divergence among closely related species. The software is free and available at the web page http://www.ipez.es/index%20ingles.html. We suggest increased international collaboration to introduce more species into IPez. (literal)

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