Application of Feature Selection Techniques to Morphological Prototyping of West African Taurine Cattle

Feature selection provides an accurate understanding of the problem under study. However, finding a stable and reproducible set of parameters that can win the confidence of experts in the field remains a challenge. In this study, we used a three-stage selection method to identify an efficient and robust group of variables that best describe the West African taurine cattle. Wrapper and embedded techniques were bagged and applied to 5 Machine Learning algorithms. The classification performance and robustness of the selection were assessed throughout the process. Finally, 12 out of 50 features were selected, and they were able to perform 96% precision as well as 97% accuracy, which is a significant improvement the results obtained on the initial dataset. The pairwise similarity and overall stability scores also increased. The resulting features were sufficiently robust to form a morphological prototype of lobi taurines.