Use of Mars Data Mining Algorithm Based on Training and Test Sets in Determining Carcass Weight of Cattle in Different Breeds

Çanga, Demet

doi:10.15832/ankutbd.818397

Cited by 4 publications

(8 citation statements)

References 26 publications

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“…The difference can be attributed to the animal species. Çanga [ 33 ] mentioned that the MARS algorithm could allow livestock breeders to obtain effective clues by using independent variables such as breed, age and body weight in estimating hot carcass weight with the determination coefficient of 0.975. Şengül et al [ 34 ] mentioned that MARS and Bagging MARS algorithms revealed correct results according to the goodness-of-fit statistics, and it has been determined that the MARS algorithm gives better results in live weight modeling.…”

Section: Discussionmentioning

confidence: 99%

Use of Multivariate Adaptive Regression Splines Algorithm to Predict Body Weight from Body Measurements of Anatolian buffaloes in Türkiye

Ağyar

Tirink

Önder

et al. 2022

Animals

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Anatolian buffalo is an important breed reared for meat and milk in various regions of Türkiye. The present study was performed to estimate body weight (BW) from several body measurements, such as tail length (TL), shoulder height (SH), withers height (WH), body length (BL), chest circumference (CC), shank diameter (SD) and birth weight (BiW). The data set was taken from Muş Province of Türkiye. In this respect, 171 Anatolian buffaloes were used. To estimate the BW, different proportions of the training and test sets were used with the MARS algorithm. The optimal MARS was determined at a proportion of %70-%30. The MARS model displays the heaviest BW that can be produced by Anatolian buffalo according to tail length, body length, chest circumference and shoulder height. In conclusion, it could be suggested that the MARS algorithm may allow animal breeders to obtain an elite population and to determine the body measurements affecting BW as indirect selection criteria for describing the breed description of Anatolian buffalo and aiding sustainable meat production and rural development in Türkiye.

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Section: Discussionmentioning

confidence: 99%

Use of Multivariate Adaptive Regression Splines Algorithm to Predict Body Weight from Body Measurements of Anatolian buffaloes in Türkiye

Ağyar

Tirink

Önder

et al. 2022

Animals

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“…To further optimize the target responses and to ensure optimum milk yield for the tested dependent variables, the YieldAutumn/winter/spring/summer estimation equations was first defined by looking at the MARS equation in Table 3. First of all, when the four prediction models are examined by looking at both Table 3 and the prediction equations, the sign differences regarding some coefficients of the same basic functions are remarkable (Akin et al, 2020b;Çelik and Yılmaz 2018;Çanga 2022;Çanga and Boğa, 2019). Since the threshold value for the lactation day, which is one of the variables discussed in Table 2, is LD=159, in the case of max(0, MDN -89), there is an increase of 16.16 units and 1.78 units in YieldAutumn and YieldSpring milk yield, respectively; Yieldwinter and Yieldsummer milk yields decreased by 9.36 and 4.02 units, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…MARS algorithm used by Friedman (1999) to capture nonlinear relationships between predictors and response variable(s) is a powerful approach that does not require assumptions about functional relationships between dependent and input variables. The model that emerges as the weighted total basic function including the BFi (x) function is given by Equation 1 below (Akin et al, 2020a;Eyduran et al, 2020;Çanga and Boğa 2020;Çanga 2022).…”

Section: Multivariate Adaptive Regression Spline (Mars)mentioning

confidence: 99%

Forecasting Seasonal Milk Production Using MARS Algorithm for Multiple Continuous Responses in Holstein Dairy Cattles

Çanga Boğa,

Boğa,

Bulut

2024

Black Sea Journal of Agriculture

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In this study, seasonal milk yield estimation will be made using multivariate adaptive regression spline (MARS) algorithm for multiple continuous responses in dairy cattle (Holstein hybrid). For the research, milking records for the years 2020-2021 were collected from 157 dairy animals using Holstein hybrid dairy cattle from a research farm in Konya, Turkey. The amount of feed given in this experiment was not changed and the effect of the season on the estimation of milk yield was investigated in the study. The analyzed independent variables used in the study were pregnancy status (PS), lactation number (LD), age of cows (months), average seven-day milk yield (7-Day Average Milk-SDMY), last lactation milk yield (last_MY), number of inseminations (IN), peak yield (Pik_Yield) and target variables were calculated as (YieldAutumn/winter/spring/summer (Kg) = Mean milk mean of season. In this context, the ehaGoF package was used to measure the prediction performance of the simultaneous MARS model established with the earth package for Mars analysis. MARS estimation equations obtained simultaneously for four dependent variables (multiple responses) are given. By looking at the MARS equation, the MARS model estimation equation was determined for the optimum milk yield, the threshold values, the three threshold values determined in the model were determined as LD, Age, Peak_Yield, and the corresponding values were respectively; 159 days, 39.6 (months) and 37.1 kg/day. Considering the estimation equation, it is seen that the independent variables LD, SDMY and LN are the most important variables in determining the estimation equation. It is seen that the best fitting value for the estimation equation of the dependent variables is the YieldWinter variable.

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“…The initial patterns that specify the smallest amount of the prediction model are abolished in the backward pass process, and this process is hand-me-down in the resolution to this problem [ 18 , 35 , 36 ]. The MARS algorithm is a significant instrument that can take linear and nonlinear relationships between dependent and independent variables [ 37 , 38 ]. The equation for MARS procedure utilized to predict body weight from explanatory variables is below.…”

Section: Methodsmentioning

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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Use of Mars Data Mining Algorithm Based on Training and Test Sets in Determining Carcass Weight of Cattle in Different Breeds

Abstract: This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record.

Cited by 4 publications

References 26 publications

Use of Multivariate Adaptive Regression Splines Algorithm to Predict Body Weight from Body Measurements of Anatolian buffaloes in Türkiye

Use of Multivariate Adaptive Regression Splines Algorithm to Predict Body Weight from Body Measurements of Anatolian buffaloes in Türkiye

Forecasting Seasonal Milk Production Using MARS Algorithm for Multiple Continuous Responses in Holstein Dairy Cattles

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

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