Assessing the utility value of Hucul horses using classification models, based on artificial neural networks.

Abstract

The aim of this study was to evaluate factors influencing the performance of Hucul horses and to develop a prediction model, based on artificial neural (AI) networks for predict horses’ classification, relying on their performance value assessment during the annual Hucul championships. The Feedforward multilayer artificial neural networks, learned using supervised methods and implemented in Matlab programming environment were applied. Artificial neural networks with one and two hidden layers with different numbers of neurons equipped with a tangensoidal transition function, learned using the Levenberg-Marqiuardt method, were applied for the analysis. Although results showed that 7-year-old horses had the highest number of wins, the 11-year-old horses were observed to have had the best results when accessed relative to the total number of horses for a given year. Although horses from the Hroby line had the most starts in 2009–2019, those of the Goral line had the most wins. While predicting the horses’ efficiency for the first 6 positions during the utility championship, the neural network consisting of 12 neurons in hidden layer performed the best, obtaining 69,65% efficiency. The highest horse efficiency classification was obtained for the four-layered network with 12 and 8 neurons in the hidden layers. An 81.3% efficiency was obtained while evaluating the correctness of the prediction for horses occupying positions 1 to 3. The use of AI seems to be indispensable in assessing the performance value of Hucul horses. It is necessary to determine the relation between horses’ traits and their utility value by means of trait selection methods, accompanied with expert advice. It is also advisable to conduct research using deep neural networks.