TY  - JOUR
TI  - Phenotypic Characterization of Hair and Honamli Goats Using Classification
Tree Algorithms and Multivariate Adaptive Regression Spline (MARS)
AB  - Some morphological and physiological data are needed to scientifically describe animals and distinguish breeds from one
another. Except for those who are not experts in the field, it is difficult to distinguish goat breeds from each other. Using data mining
algorithms, this study aimed to develop a new phenotypic characterization for Honamli and Hair goats via some body measurement
characteristics. In the study, some body characteristics of the Hair goat (65 animals) and the Honamli goat (83 animals) were used as
independent variables. Th e dependent variable of the data mining algorithms, on the other hand, was defined as the binary response
variable of Honamli and Hair breeds. Th e success of the CHAID, Exhaustive CHAID, CART, QUEST, and MARS algorithms in breed
discrimination was determined at 87.80%, 85.80%, 87.80%, 77.00%, and 88.51%, respectively, while the area under the ROC curve
was detected 0.880, 0.853, 0.868, 0.784, and 0.942, respectively, and Cohen’s Kappa coefficient (κ) 0.755, 0.711, 0.749, 0.549 and 0.739,
respectively. As a result, the phenotype characterization of Honamli and Hair goats, whose morphological distinctions could not be made
exactly, in MARS and CHAID algorithms, achieved with high success compared to other methods. Th e present study showed that Honamli
and Hair goats may be distinguished by suitable statistical algorithms based on morphological data, which can be integrated with goat
breeding studies to detect the origin of breeding animals.
AU  - ALTAY, Yasin
DO  - 10.9775/kvfd.2022.27163
PY  - 2022
JO  - Kafkas Üniversitesi Veteriner Fakültesi Dergisi
VL  - 28
IS  - 3
SN  - 1300-6045
SP  - 401
EP  - 410
DB  - TRDizin
UR  - http://search/yayin/detay/532237
ER  -