Case study: Comparing the use of nonlinear discriminating analysis and Artificial Neural Networks in the classification of three fish species: acaras (Geophagus brasiliensis), tilapias (Tilapia rendalli) and mullets (Mugil liza)

This study used the Discriminant Analysis statistical technique and Artificial Neural Networks, multilayer perceptron, in the classification of three fish species sampled in the state of Rio de Janeiro, Brazil: Geophagus brasiliensis (acaras), Tilapia rendall (tilapias) and Mugil liza (mullets). These fish were sexed when possible, weighed, measured, and had their Gonadosomatic and Hepatosomatic Indices calculated, as well as their Condition Factor. The use of an Artificial Neural Network (ANN) presented satisfactory results, even though the groups were composed of very diverse-sized animals. Without the need for non-violation assumptions and other considerations, the Artificial Neural Network was found to be the excellent alternative to classification problems of unbalanced data, such as the one presented in this study.