Examples of the Use of Data Mining Methods in Animal Breeding

Abstract

Data mining techniques involve mainly searching for various relationships in large data sets. However, they can also be used in a much narrower range, sometimes as an alternative to classical statistics. The characteristic feature of these models is the use of a specific strategy, usually requiring the division of data into training set, sometimes also verification set, which enable the evaluation of the model quality as well as a test set for checking its prognostic or classification abilities. Among many different methods belonging to data mining, the following can be distinguished: the general models of classification and regression trees (G_Trees), general CHAID (Chi-square Automatic Interaction Detection) models, interactive classification and regression trees (also with boosting – Boosted Trees), random forest, MARS (Multivariate Adaptive Regression Splines), artificial neural networks (ANN), other machine learning methods such as: naive Bayes classifier (NBC), support vector machines (SVM), k-nearest neighbors (k-NN) and other regarded (or not) by different authors as data mining techniques. These methods are more and more frequently applied to various issues associated with animal breeding and husbandry.