Use of feature selection algorithm to determine the most effective fatty acids associated with milk fat of Holstein cows

Understanding the regulation of milk fat biosynthesis is vital to the development of nutritional strategies to increase the nutritional value of milk, reduce milk-energy out-put and improve the energy balance of lactating cows. The objective of this study was to determine the most important fatty acids affecting milk fat of Holstein cows using feature selection algorithm. Feature selection algorithm is one of the data mining methods which is used to determine the most effective and best-known parameters predicting targeted trait. A total of 3072 raw data and three important methods of feature selection with six models was used. Data was analyzed using the WEKA Software (v. 3.8). Results indicated that Ranker method of feature selection algorithm is the most appropriate method to select the most important fatty acids affecting milk fat using LMT classification with minimized error. Accordingly, n-3 C20:3 (eicosatrienoic acid), n-6 C20:2 (eicosadienoic acid), total trans C16:1, trans-10 C16:1, trans-6, 7, 8 C16:1, total trans monounsaturated fatty acids, trans-11, cis-15 C18:2, total trans C18:1, n-6 C20:4, and cis-12 C18:1 were determined as the most important fatty acids affecting milk fat. Feature selection algorithm showed that fatty acids other than trans-10, cis-12 conjugated linoleic acid and trans-10 C18:1 is associated with milk fat biosynthesis which is necessary to be further characterized their biological importance in the next future studies.