Logistic regression and fuzzy logic as a classification method for feral fish sampling sites
| Autor: | Roberta L. Ziolli, Terezinha Ferreira de Oliveira, Antonio Silveira, João Marcelo Brazão Protázio, Rachel Ann Hauser-Davis |
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| Rok vydání: | 2012 |
| Předmět: |
Statistics and Probability
biology Fishing Sampling (statistics) Logistic regression computer.software_genre biology.organism_classification Fuzzy logic Regression Variable (computer science) Nile tilapia Statistics Data mining Statistics Probability and Uncertainty computer General Environmental Science Mathematics Multinomial logistic regression |
| Zdroj: | Environmental and Ecological Statistics. 19:473-483 |
| ISSN: | 1573-3009 1352-8505 |
| DOI: | 10.1007/s10651-012-0196-1 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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