Th alli Koyunlarında Biyometrik Ölçümlerden Vücut Ağırlığı Tahmini İçin Bayesian Regularized Neural Network, Random Forest Regresyon, Support Vector Regresyon ve Çok Değişkenli Regresyon Uzanımları Algoritmalarının Karşılaştırılması

Tirink, Cem

doi:10.9775/kvfd.2022.27164

Cited by 5 publications

(4 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second process develops a regression tree for each sample with un-pruned aspects. The last process is to predict the latest data from the constructed tree [8].…”

Section: Discussionmentioning

confidence: 99%

“…For this reason, body weight (BW) appears as the most critical information in production systems, as it will vary depending on many financial characteristics [6,7]. Accurate BW prediction is a basis in animal science studies, such as animal healthcare management, animal husbandry, and determining drug doses and feeding optimization [8]. BW estimation poses a complex challenge in identifying and modeling many processes in animal breeding due to many factors that include computationally demanding situations, from determining herd management strategies to genetic selection.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

Gomez-Vazquez,

Tırınk,

Cruz-Tamayo

et al. 2024

Animals

View full text Add to dashboard Cite

This study aims to use advanced machine learning techniques supported by Principal Component Analysis (PCA) to estimate body weight (BW) in buffalos raised in southeastern Mexico and compare their performance. The first stage of the current study consists of body measurements and the process of determining the most informative variables using PCA, a dimension reduction method. This process reduces the data size by eliminating the complex structure of the model and provides a faster and more effective learning process. As a second stage, two separate prediction models were developed with Gradient Boosting and Random Forest algorithms, using the principal components obtained from the data set reduced by PCA. The performances of both models were compared using R2, RMSE and MAE metrics, and showed that the Gradient Boosting model achieved a better prediction performance with a higher R2 value and lower error rates than the Random Forest model. In conclusion, PCA-supported modeling applications can provide more reliable results, and the Gradient Boosting algorithm is superior to Random Forest in this context. The current study demonstrates the potential use of machine learning approaches in estimating body weight in water buffalos, and will support sustainable animal husbandry by contributing to decision making processes in the field of animal science.

show abstract

“…The second process develops a regression tree for each sample with un-pruned aspects. The last process is to predict the latest data from the constructed tree [8].…”

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

Gomez-Vazquez,

Tırınk,

Cruz-Tamayo

et al. 2024

Animals

View full text Add to dashboard Cite

show abstract

“…Tırınk [ 54 ] compared various artificial intelligence methods such as Multivariate Adaptive Regression Splines, Random Forest Regression, Bayesian Regularized Neural Network, and Support Vector Regression algorithms to estimate body weight from biometric measurements for the Thalli sheep breed. For this aim, 270 female Thalli sheep breeds were used.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

“…Decision trees, belonging to machine learning methods, allow for predicting the level of different traits while controlling for the factors affecting it. The application of these techniques in various zootechnical analyses is increasing [ 16 , 17 , 18 , 19 , 20 ]. The obtained information allows for identifying individuals with potentially low values of a given trait.…”

Section: Introductionmentioning

confidence: 99%

Classification of Daily Body Weight Gains in Beef Calves Using Decision Trees, Artificial Neural Networks, and Logistic Regression

Grzesiak

Zaborski

Pilarczyk

et al. 2023

Animals

View full text Add to dashboard Cite

The aim of the present study was to compare the predictive performance of decision trees, artificial neural networks, and logistic regression used for the classification of daily body weight gains in beef calves. A total of 680 pure-breed Simmental and 373 Limousin cows from the largest farm in the West Pomeranian Province, whose calves were fattened between 2014 and 2016, were included in the study. Pre-weaning daily body weight gains were divided into two categories: A—equal to or lower than the weighted mean for each breed and sex and B—higher than the mean. Models were developed separately for each breed. Sensitivity, specificity, accuracy, and area under the curve on a test set for the best model (random forest) were 0.83, 0.67, 0.76, and 0.82 and 0.68, 0.86, 0.78, and 0.81 for the Limousin and Simmental breeds, respectively. The most important predictors were daily weight gains of the dam when she was a calf, daily weight gains of the first calf, sex of the third calf, milk yield at first lactation, birth weight of the third calf, dam birth weight, dam hip height, and second calving season. The selected machine learning models can be used quite effectively for the classification of calves based on their daily weight gains.

show abstract

Th alli Koyunlarında Biyometrik Ölçümlerden Vücut Ağırlığı Tahmini İçin Bayesian Regularized Neural Network, Random Forest Regresyon, Support Vector Regresyon ve Çok Değişkenli Regresyon Uzanımları Algoritmalarının Karşılaştırılması

Cited by 5 publications

References 0 publications

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

Prediction of Body Weight by Using PCA-Supported Gradient Boosting and Random Forest Algorithms in Water Buffaloes (Bubalus bubalis) Reared in South-Eastern Mexico

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

Classification of Daily Body Weight Gains in Beef Calves Using Decision Trees, Artificial Neural Networks, and Logistic Regression

Contact Info

Product

Resources

About