Determination of early breeder in goldfish (Carassius auratus Linn.) with learning vector quantization, probabilistic and pattern recognition neural networks

Abstract

It is common practice to categorize growing fish according to quality in fish farming centers. For such an application, experienced and estimating people are needed. By pre-selecting the right fish for the ornamental fish industry, it gets ahead of the competitors. This study deals with the classification of early breeder determination in goldfish using three different Artificial Neural Network (ANN) techniques. This classification model can help the fish industry assess classification risks and make the right decision. The used dataset was derived from the results of the classification section of 120 goldfish. It consisted of 7 input parameters (day, live weight, body length, head height, head width, body height, and current class). During trial, all goldfish fed by a diet contained 360 g crude protein and 4449.85 kcal metabolizable energy (kg / dry matter). The important types of classification ANNs, namely Learning Vector Quantization Neural Network (LVQNN), Probabilistic Neural Network (PNN), and Pattern Recognition Neural Network (PRNN) were employed for the machine learning scheme. The training and test performances of the ANN models were compared with the correct prediction ratio. They showed that all of the proposed ANN techniques were well at classification. However, the PRNN model was better than the LVQNN and PNN as a classifier for the breeder selection of goldfish. Therefore, the results of PRNN model such as histogram, Receiver Operating Characteristic (ROC) curve, regression, confusion matrix, accuracy, sensitivity, precision, and F₁ score were given and discussed. Furthermore, the classification of early breeder determination in goldfish were examined for days with the best PRNN.