Chapters authored
Comparison of MARS, CART, and Linear Regression Models for Prediction of Body Weight of Non-descript Indigenous Goats in Lepelle-Nkumbi Local Municipality, South Africa
In Lepelle-Nkumbi Local Municipality of South Africa, 200 none-descript indigenous goats ranging in age from one to five years were the subjects of a study that compared the live body weight predictions made by stepwise linear regression, Classification Regression Tree (CART), and Multivariate Adaptive Splines (MARS) models. Several bodily measurements, such as canonical circumference (CC), sternum height (SH), body length (BL), ear length (EL), head length (HL), head width (HW), rump length (RL), rump height (RH), and rump width (RW). The evaluation criteria included the root mean square error (RMSE), coefficient of determination (R2), to decide which model was the best. According to the results, CART outperformed the others, obtaining the lowest RMSE (3.65) and the greatest R2 (0.80). The stepwise regression model outperformed data mining algorithms in male goats. According to the study, CART is a useful statistical technique for defining requirements for producing indigenous goats that are not very special. In addition, when predicting live body weight from body measuring features, the stepwise regression model should be considered.
Part of the book: Association Rule Mining and Data Mining - Recent Advances, New Perspectives and Applications [Working title]
Use of Data Mining Algorithms in Chicken Breeding: A Systematic Review
Data mining algorithms have been performed to reveal the factors that can be used to enhance live body weight and egg weight during chicken breeding. This work was conducted to systematically review the published articles on the use of data mining algorithms in chicken breeding. ScienceDirect, Web of Science, PubMed, Google Scholar and were used for searching articles. Using the combination of chicken or chicken breeding, data mining algorithm or decision tree, body weight and egg weight as keywords. The results indicated that 8 articles were included from 120 articles were found from searching. The 8 included articles were published from 2016 to 2021 and most of them were originated from South Africa (n = 3) followed by Turkey (n = 2) with. CHAID as the most used data mining algorithm (n = 5) followed by CART (n = 4). Out of 8 included articles, 6 of them used coefficient of determination (R2) as the selection criteria and CART was found as the best model followed by the CHAID model. It is concluded that CART followed by CHAID data mining algorithms are the recommended models that might be used for improving egg production and growth performance of chickens.
Part of the book: Association Rule Mining and Data Mining - Recent Advances, New Perspectives and Applications [Working title]