CART ve CHAID Algoritmalarının Karayaka Koyun Islahında Kullanımı

Olfaz, Mustafa; Tirink, Cem; Önder, Hasan

doi:10.9775/kvfd.2018.20388

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…For instance, there is a literature comparison between the CART and NN models, whose results show that the NN model has a better effect on the cost prediction of colorectal cancer patients [26]. However, in the prediction and analysis of this literature and other literature [29,30], there is little comparative study of Kappa value from the actual value data source.…”

Section: Discussionmentioning

confidence: 99%

Comparison of Job Satisfaction Prediction Models for Construction Workers: CART vs. Neural Network

Chen

Cao

2021

Teh. vjesn.

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To establish a suitable prediction model of construction workers' job satisfaction, this study chooses the widely used models CART (Classification and Regression Tree) and NN (Neural network) in the prediction model to make a comparison and finds out the main influencing factors of construction workers' job satisfaction in occupational health and safety training. Through the investigation and analysis of 280 cases of empirical data, it is found that the CART model based on Kappa value and Accuracy of categorical variables have a better prediction effect, and the main factors affecting job satisfaction are job categories, working days per week and the latest training time. The main innovation of this paper is to add the actual value set of empirical data on the basis of the usual training set, verification set, test set and prediction set, and draw a conclusion by comparing the predicted value with the actual value of kappa.

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Section: Discussionmentioning

confidence: 99%

Comparison of Job Satisfaction Prediction Models for Construction Workers: CART vs. Neural Network

Chen

Cao

2021

Teh. vjesn.

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“…In comparison of the model performances, the following goodness of fit criteria were used [ 27 , 28 , 33 , 36 , 45 ]:…”

Section: Methodsmentioning

confidence: 99%

“…However, various data mining and machine learning algorithms do not need these assumptions [ 24 , 25 ]. In this context, many studies conducted within the scope of multivariate statistical methods have been carried out on different breeds and species, in which characteristics such as birth weight, live weight, weaning weight, and final weight are tried to be estimated [ 26 – 28 ]. The main purpose of trying to predict such different traits is that these traits vary among animal materials.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed

et al. 2023

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The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of final body weight for the Romane sheep breed. For this purpose, classification and regression tree (CART), multivariate adaptive regression splines (MARS), and support vector machine regression (SVR) algorithms were used for training (80%) and testing (20%) sets. Different data mining and machine learning algorithms were used to predict final body weight of 393 Romane sheep (238 female and 155 male animals) were used with different artificial intelligence algorithms. The best prediction model was obtained by CART model, both training and testing set. Constructed CART models indicated that sex, suckling weight, weaning weight, age of weaning weight, and age of final weight could be used as an indirect selection measure to get a superior sheep flock on the final body weight of Romane sheep. If genetically established, the Romane sheep whose sex is female, age of final weight is over 142 days, and weaning weight is over 28 kg could be chosen for affording genetic improvement in final body weight. In conclusion, the usage of CART procedure may be worthy of reflection for identifying breed standards and choosing superior sheep for meat yield in France.

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“…In general, animal breeding aims to genetically improve the next generation in order to increase the quantity and quality of economic traits in certain environments [1]. Traditional genetic improvement in farm animals has successfully predicted breeding values using phenotypes and pedigree information.…”

Section: Introductionmentioning

confidence: 99%

Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

Önder,

Sitskowska,

Kurnaz

et al. 2023

Animals

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The objective of our study was to evaluate the predictive ability of a multi-trait genomic prediction model that accounts for interactions between marker effects to estimate heritability and genetic correlations of traits including 305-day milk yield, milk fat percentage, milk protein percentage, milk lactose percentage, and milk dry matter percentage in the Polish Holstein Friesian cow population. For this aim, 14,742 SNP genotype records for 586 Polish Holstein Friesian dairy cows from Poland were used. Single-Trait-ssGBLUP (ST) and Multi-Trait-ssGBLUP (MT) methods were used for estimation. We examined 305-day milk yield (MY, kg), milk fat percentage (MF, %), milk protein percentage (MP, %), milk lactose percentage (ML, %), and milk dry matter percentage (MDM, %). The results showed that the highest marker effect rank correlation was found between milk fat percentage and milk dry matter. The weakest marker effect rank correlation was found between ML and all other traits. Obtained accuracies of this study were between 0.770 and 0.882, and 0.773 and 0.876 for MT and ST, respectively, which were acceptable values. All estimated bias values were positive, which is proof of underestimation. The highest heritability value was obtained for MP (0.3029) and the lowest heritability value was calculated for ML (0.2171). Estimated heritability values were low for milk yield and milk composition as expected. The strongest genetic correlation was estimated between MDM and MF (0.4990) and the weakest genetic correlation was estimated between MY and ML (0.001). The genetic relations with milk yield were negative and can be ignored as they were not significant. In conclusion, multi-trait genomic prediction can be more beneficial than single-trait genomic prediction.

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CART ve CHAID Algoritmalarının Karayaka Koyun Islahında Kullanımı

Cited by 13 publications

References 13 publications

Comparison of Job Satisfaction Prediction Models for Construction Workers: CART vs. Neural Network

Comparison of Job Satisfaction Prediction Models for Construction Workers: CART vs. Neural Network

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed

Multi-Trait Single-Step Genomic Prediction for Milk Yield and Milk Components for Polish Holstein Population

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