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Classification accuracy of algorithms for blood chemistry data of three aquaculture-influenced marine fish species

Čož-Rakovac, Rozelinda; Topić Popović, Natalija; Šmuc, Tomislav; Strunjak-Perović, Ivančica; Jadan, Margita (2009) Classification accuracy of algorithms for blood chemistry data of three aquaculture-influenced marine fish species. Fish Physiology and Biochemistry, 35 (4). pp. 641-647. ISSN 0920-1742

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Abstract

The aim of this study was determination and discrimination of biochemical data between three aquaculture-influenced marine fish species (sea bass, Dicentrarchus labrax ; sea bream, Sparus aurata L ; mullet, Mugil spp.) based on machine learning methods. The approach relying on machine learning methods gives more usable classification solutions and provides better insight into the collected data. So far, these new methods were applied to the problem of discrimination of blood chemistry data with respect to season and feed of one single species. This is the first time that these classification algorithms were used as a framework for rapid differentiation between three fish species. Among the machine learning methods used, decision trees provided the clearest model, which correctly classified 210 samples or 85.71 %, and incorrectly classified 35 samples or 14.29 % and clearly identified three investigated species regarding to their biochemical traits.

Item Type: Article
Uncontrolled Keywords: machine learning techniques; sea bass; sea bream; mullet; plasma biochemistry
Subjects: NATURAL SCIENCES > Biology
Divisions: Division of Electronics
Division of Materials Chemistry
Projects:
Project titleProject leaderProject codeProject type
Substanična biokemijska i filogenetska raznolikost tkiva riba, rakova i školjaka-Rozelinda Čož-Rakovac098-1782739-2749MZOS
Strojno učenje prediktivnih modela u računalnoj biologiji-Tomislav Šmuc098-0000000-3168MZOS
Depositing User: Natalija Topić Popović
Date Deposited: 07 Dec 2017 15:18
Last Modified: 07 Dec 2017 15:18
URI: http://fulir.irb.hr/id/eprint/3773
DOI: 10.1007/s10695-008-9288-0

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