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Logistic regression and fuzzy logic as a classification method for feral fish sampling sites

Logistic regression and fuzzy logic as a classification method for feral fish sampling sites

Environmental and Ecological Statistics 19(4): 473-483

This study presents a classification method combining logistic regression and fuzzy logic in the determination of sampling sites for feral fish, Nile Tilapia (Tilapia rendalli). This method statistically analyzes the variable domains involved in the problem, by using a logistic regression model. This in turn generates the knowledge necessary to construct the rule base and fuzzy clusters of the fuzzy inference system (FIS) variables. The proposed hybrid method was validated using three fish stress indices; the Fulton Condition Factor (FCF) and the gonadossomatic and hepatossomatic indices (GSI and HSI, respectively), from fish sampled at 3 different locations in the Rio de Janeiro State. A multinomial logistic regression allowed for the FIS construction of the proposed method and both statistical approaches, when combined, complemented each other satisfactorily, allowing for the construction of an efficient classification method regarding feral fish sampling sites that, in turn, has great value regarding fish captures and fishery resource management.

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Accession: 036371128

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DOI: 10.1007/s10651-012-0196-1

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