Phenotypic Characterization of Hair and Honamli Goats by Using Classification Trees Algorithms and Multivariate Adaptive Regression Splines (Mars)

Abstract

In order to meet the food demand of the increasing world population, it is very important to define the animal breeds and species raised in tropical and subtropical regions and to organize breeding programs for this. Discrimination animal breeds by morphological classification are a widely used method for a century. Although Honamli and Hair goats are very similar to each other morphologically, they can be subjectively distinguished by experienced breeders with some distinctive morphological markers. In the current study, certain body characteristics of Hair goats, which have a large portion of the population in Turkey, and Honamli goat, which has recently been registered as a new breed were used. Phenotypic characterization of these breeds has been made using data mining methods such as Classification and regression tree (CART), chi-square automatic interaction detector (CHAID), Exhaustive CHAID, Quick Unbiased, Efficient Statistical Tree, (QUEST), and multivariate adaptive regression splines (MARS) algorithms. In other words, the current study is the first data mining algorithms used for phenotypic characterization in Hair and Honamli goat breeds. Goats’ morphological characteristics such as live weight (LW), withers height (WH), back height (BH), rump height (RH), chest Depth (CD), body length (BL), chest girth (CG), leg girth (LG), head length (HL), fore head (FH), ear length (EL), and tail length (TL), used in diagnosis of discrimination on breeds, were used as a binary response variable in Honamli and Hair breeds. Here, the independent variables used in data mining algorithms are the morphological characteristics of goats. CHAID, Exhaustive CHAID, CART, QUEST, and MARS were used as data mining algorithms to make an accurate decision in detecting effective morphological traits in breed discrimination. The success of the CHAID, Exhaustive CHAID, CART, QUEST and MARS algorithms in breed discrimination is 87.80%, 85.80%, 87.80%, 77.00%, and 88.51%, respectively, while the area under the ROC curve is 0.880, 0.853, 0.868, 0.784 and 0.942, respectively. As a result, using data mining methods for some body measurements of Honamli and Hair goats, whose morphological distinction is not exactly accurate, phenotype characterization separation was performed with high success in MARS and CHAID algorithms compared with the other methods. The outputs of this study can be used for breeding material by enabling pure Honamli goat breeding. Also, data mining algorithms can be included in gene resource conservation programs.

Phenotypic characterization of indigenous Cyprus Native Hair Goat breed using quantitative and qualitative trait analysis

“…Some morphological body measurements obtained from hair goat studies conducted in Türkiye are compared in TABLE V [26,27,28]. In all the studied Regions included in TABLE V, the Cyprus domestic Goats are larger than these Goats in both sexes.…”

Siyah Alaca Sığırlarda Farklı Büyüme ve Gelişme Dönemlerindeki Vücut Ölçülerinden Canlı Ağırlık Tahmininde Kullanılan Veri Madenciliği Algoritmalarının Karşılaştırılması

“…Bu bağlamda vücut ölçüleri, cinsiyet, verim türü, yaş ve ırk gibi önemli faktörlere göre değişiklik göstermektedir (Pesmen ve Yardimci, 2008). Günümüze kadar canlı ağırlık ve vücut ölçüleri arasındaki ilişkiyi yorumlamak amacıyla; kanonik korelasyon (Kara-bacak, Altay, ve Aytekin, 2019) yapay sinir ağları ve çoklu doğrusal regresyon yöntemleri (Akkol, Akilli, ve Cemal, 2017), Stepwise-regresyon yöntemi (Koç ve Akman, 2007), path analizi (Keskin, Dağ, ve Şahin, 2005) bulanık mantık yöntemi (Taşdemir, Ürkmez, ve İnal, 2011;Ameen ve Mikail, 2018), Çok Değişkenli Uyarlanabilir Regresyon Uzanımları (MARS) algoritmaları (Aytekin, Eyduran, Karadas, Aksahan, ve Keskin, 2018), CART, CHAID, Exhaustive CHAID algoritmaları (Altay, 2022) ve Bayesian Regularized Neural Network, Random Forest Regression, Support Vector Regression algoritmaları (Tırınk, 2022) gibi birçok metot kullanılmıştır.…”