Research Article
BibTex RIS Cite
Year 2022, Volume: 28 Issue: 2, 259 - 268, 25.04.2022
https://doi.org/10.15832/ankutbd.818397

Abstract

References

  • Agaoglu YS, Eyduran SP & Eyduran E (2007). Comparison of Some Pomological Characteristics of Blackberry Cultivars Growth in Ayaş Conditions. Ankara Universitesi Tarım Bilimleri Dergisi 13(1): 69-74
  • Akin M, Eyduran, SP & Eyduran E (2020). Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Plant Cell Tiss Organ Cult 140, 661–670. https://doi.org/10.1007/s11240-019-01763-8
  • Aksoy A, Erturk YE, Eyduran E and Tariq MM (2018). Comparing predictive performances of MARS and CHAID algorithms for defining factors affecting final fattening live weight in cultural beef cattle enterprises Pakistan Journal of Zoology. 50(6): 2279-2286
  • Ali M, Eyduran E, Tariq MM, Tirink C, Abbas F, Bajwa MA, Baloch MH, Nizamani AH, Waheed A, Awan MA, Shah SH, Ahmad Z and Jan S (2015). Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pakistan J. Zool., 47: 1579-1585
  • Anonim. Comparing different cattle breeds Use the 2016 across-breed EPD table. 2020; http://www.beefmagazine.com/ print/15608 (Erişim tarihi: 31.05.2020)
  • Aytekin I, Eyduran E, Koksal K, Akşahan R, Keskin I (2018). Prediction of fattening final liveweight from some body measurements and fattening period in young bulls of crossbred and exotic breeds using MARS data mining algorithm. Pakistan J. Zoology. 50(1): 189-195
  • Canga D & Boga M (2020). Determination of the Effect of Some Properties on Egg Yield with Regression Analysis Met-hod Bagging Mars and R Application. Turkish Journal of Agriculture - Food Science and Technology 8(8): 1705-1712
  • Canga D & Boga M (2019). Use of MARS in livestock and An application. III. International Scientific and Vocational Studies Congress – Science and Health Full Text Paper Book; Nevşehir, Turkey p. 31-37
  • Canga D, Yavuz E & Efe E (2019). Use of MARS Data Mining Algorithm for Egg Weight Estimation. The International Congress on Domestic Animal Breeding Genetics and Husbandry-19 Full Text Paper Book; Prague, Czechia; 2019.p.127
  • Celik S & Boydak E (2020). Descrıptıon of The Relatıonshıps Between Dıfferent Plant Characterıstıcs In Soybean Usıng Multıvarıate Adaptıve Regressıon Splınes Mars Algorıthm, Journal Of Animal And Plant Sciences. Vol. 30, No. 2, Pp. 431–441
  • Celik S, Eyduran E, Tatliyer A, Karadas K, Kara MK et al (2020) Comparing predictive performances of some nonlinear functions and Multivariate Adaptive Regression Splines (MARS) for describing the growth of Daera Dın Panah (DDP) goat in Pakistan. Pakistan J. Zoology 50(3): 1187-1190
  • Celik S & Yilmaz O (2018). Prediction of Body Weight of Turkish Tazi Dogs using Data Mining Techniques: Classification and Regression Tree (CART) and Multivariate Adaptive Reg-ression Splines (MARS). Pakistan Journal of Zoology 50(2). doi:10.17582/journal.pjz/2018.50.2.575.583
  • Chou SM, Lee TS, Shao YE, Chen IF (2004). Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications 27:133-142
  • Craven P &Wahba G (1979). Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik 31: 377-403
  • Deconinck E, Xu QS, Put R, Coomans D, Massart DL, et al (2005). Prediction ofgastro-intestinal absorption using multivariate adaptive regression splines. Journal of Pharmaceutical and Biomedical Analysis; 39: 1021–1030
  • Devijver Pierre A.; Kittler, Josef (1982). Pattern Recognition: A Statistical Approach. London, GB: Prentice-Hall. ISBN 0-13-654236-0
  • Duru S & Sak H (2017). Fattening Performance and Carcass Characteristics of Simmental, Aberdeen Angus, Hereford, Limousine and Charolais Cattle Breeds in Turkey. Turkish Journal of Agriculture-Food Science and Technology. 5(11):1383-1388
  • Efe E, Bek Y & Şahin M (2000). Spss’te Çözümleri ile İstatistik Yöntemler II. Kahramanmaraş Sütçü İmam Üniversitesi Rektörlüğü. Yayın No: 73, Ders Kitapları Yayın No: 9, Turkey: Kahramanmaraş
  • Eyduran E, Akin M & Eyduran SP (2019). Application of multivariate adaptive regression splines in agricultural sciences through R Software. Nobel Bilimsel Eserler. Sertifika No:20779, Ankara ISBN: 978-605-2149-81-2
  • Eyduran E, Akkus O, Kara MK, Tırınk C, Tariq MM (2017a). Use of Multivariate Adaptive Regression Splines (Mars) in Predicting Body Weight from Body Measurements in Mengali Rams. International Conference on Agriculture, Food, Veterinary and Pharmacy Sciences, Cappadocia Turkey, 415
  • Eyduran E & Duman H (2020). R Yazilimi ile Multivariate Adaptive Regression Splines (Mars) Uygulamasi Ders Notlari Eyduran E & Gulbe A. ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0 2019. https://CRAN.R-project.org/package=ehaGoF
  • Eyduran E, Sevgenler H, Akın M, Eyduran SP (2018). Usage multivariate adaptive regression splines for predicting continuous responses. Animal and Plant Sciences. International Agricultural Science Congress Full Text Paper Book. Van, Turkey
  • Eyduran E, Tirink C, Karahan AE, Türkoğlu M, Tariq MM (2017b). Comparison of Predictive Performances of MARS and CART Algorithms through R Software. International Con-ference on Computational ans Statistical Methods in Applied Sciences, Samsun Turkey, 181
  • Friedman JH (1991). Multivariate Adaptive Regression Splines. Annls. Stat. 19: 1-141. https://doi. org/10.1214/aos/1176347963
  • Geisser, Seymour (1993). Predictive Inference. New York, NY: Chapman and Hall. ISBN 978-0-412-03471-8
  • Grzesiak W, Zaborski D, Sablik P, Żukiewicz A, Dybus A and Szatkowska I (2010). Detection of cows with insemination problems using selected classification models. Comput. Electron. Agric., 74: 265-273. https://doi.org/10.1016/j.compag.2010.09.001
  • Hastie T, Tibshirani R & Friedman J (2001). The Elements of Statistical Learning: Date mining, inference, and prediction. Springer. New York
  • Kayri, M (2010). The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines, Iranian J Publ Health 2010; 39: 51-63
  • Kibet CE (2012). A Multıvarıate Adaptıve Regressıon Splınes Approach to Predıct the Treatment Outcomes of Tuberculosıs Patıents in Kenya, Science in Biometry to The University of Nairobi, Kenya
  • Kohavi R (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann. 2 (12): 1137–1143. CiteSeerX 10.1.1.48.529
  • Kor A & Ertuğrul M (2000) Canlı hayvanda karkas kompozisyonu tahmin yöntemleri. Hayvansal Üretim 41(1) Kumlu S (2000). Hayvancılık Örgütleri. Türkiye Damızlık Sığır Yetiştiricileri Merkez Birliği Yayınları. Yayın No: 2, Ankara
  • Milborrow S (2018). Milborrow. Derived from mda:mars by T. Hastie and R. Tibshirani. İnternet url: https://CRAN.R-project.org/package=earth (10.10.2018)
  • Milborrow S (2011). Derived from MDA: MARS by T. Hastie and Tibshirani earth: Multivariate adaptive regression splines, R package
  • Mukhopadhyay A & Iqbal A (2000). Prediction of mechanical property of steel strips using multivariate adaptive regression splines. Journal of Applied Statistics 36(1): 1-9.[ElectronicJournal],http://www.informaworld.com/smpp/title~content=t713428038.
  • Oğuz A (2014). Çok Değişkenli Uyarlanabilir Regresyon Zincirlerinin İrdelenmesi Ve Bir Uygulama Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Erzincan Üniversitesi, Erzincan Türkiye, 2014
  • Orhan H, Teke EÇ & Karcı Z (2018). Application of Multivariate Adaptive Regression Splines (MARS) for Modeling the Lactation Curves. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi . 21(3):363-373. (in Turkish with an abstract in English) doi: 10.18016/ksudobil.334237
  • Put R, Xu QS, Massart DL, Vander Heyden (2004). Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure–retention relationship studies. Journal of Chromatography A. 1055: 11–19
  • R Core Team (2014). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, http://www.R-project.org
  • Ripley BD (1996) Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press, p. 354 Salford Systems (2001). MARSTM User Guide. Salford Systems, San Diego, CA, USA
  • Seker İ, Köseman A, Seker P, Baykalır Y (2017). Carcass Grading System Used in the United States for Beef Carcass Quality Evaluation. 15(2):192-203. (in Turkish with an abstract in English) doi: 10.24323/akademik-gida.333676
  • Sengul T, Celik S, Eyduran E, Iqbal F (2020). Predicting egg production in Chukar partridges using nonlinear models and multivariate adaptive regression splines (MARS) algorithm. European Poultry Science. 84
  • Sevgenler H (2019). Keçilere Ait Kimi Özelliklerin Canlı Ağırlık Üzerindeki Etkilerini Belirlemek Amacıyla Kullanılan Veri Madenciliği Algoritmalarının (Cart, Chaıd Ve Mars) Karşılaştırılması. Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Iğdır Üniversitesi, Iğdır, Türkiye
  • Sevimli, Y (2009). Çok değişkenli uyarlanabilir regresyon uzanımlarının bir split-mouth çalışmasında uygulaması Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Marmara Üniversitesi, İstanbul, Türkiye
  • Tunay KB (2001). Türkiye’de paranın gelir dolaşım hızlarının MARS yöntemiyle tahmini, ODTÜ Gelişme Dergisi 2001; 28 (3-4): 431-454
  • Xu QS, Daeyaert F, Lewi PJ, Massart DL (2006). Studies of relationship between biological activities and HIV Reverse Transcriptase Inhibitors by Multivariate Adaptive Regression Splines with Curds and Whey. Chemometrics and Intelligent Laboratory Systems. 82: 24-30
  • Yıldız G (2020). Ankara Üniversitesi, Veteriner Fakültesi, Hayvan Besleme ve Beslenme Hastaliklari Anabilim Dalı; 2020. https://acikders.ankara.edu.tr/pluginfile.php/46643/mod_resource/content/0/BESI-SIGIRLARININ-BESLENMESI-GULTEKIN-YILDIZ.pdf
  • Zaborski D, Ali M, Eyduran E, Grzesiak W, Tariq MM, et al (2019). Prediction of Selected Reproductive Traits of Indigenous Harnai Sheep under the Farm Management System via various Data Mining Algorithms. Pakistan J. Zoo. 51(2):421-431. doi:10.17582/journal.pjz/2019.51.2.421.43
  • Zahrádková R, Bartoň L, Bureš D, Teslík V, Kudrna V (2010). Comparison of growth performance and slaughter characteristics of Limousine and Charolais heifers. Archiv Tierzucht; 2010. 53(5): 520-528
  • Zhang W& Goh AT (2016). Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers 7(1): 45-52

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

Year 2022, Volume: 28 Issue: 2, 259 - 268, 25.04.2022
https://doi.org/10.15832/ankutbd.818397

Abstract

This research was carried out with the purpose of estimating hot carcass weight by using parameters such as race, carcass weight and age with Multivariate Adaptive Regression Spline (MARS) algorithm. To achieve this goal, 700 cattle data belonging to the years 2017-2018, which were taken in equal numbers from 7 different breeds, were used. A total of 700 data were used, taking equal numbers of data from each breed. In order to test the accuracy of the model created in the research, the data set was divided into two data subsets as training and test subsets. In order to test the compatibility of these separated subsets with the MARS model, a new package program named “ehaGoF” which estimates 15 goodness of fit criteria was used. According to the analysis results, the MARS model with the smallest SDRATIO (0.157, 0.130) and the highest determination coefficient (R2) (0.975, 0.983) of the training and test sets, respectively, was determined. Looking at the other fit values, it is seen that the training and test set are quite compatible. In terms of hot carcass weight among the breeds, it was determined that the Limousine race performed higher than the other breeds. As a result, the implementation of the MARS algorithm can allow livestock breeders to obtain effective clues by using independent variables such as breed, age, and body weight in estimating hot carcass weight.

References

  • Agaoglu YS, Eyduran SP & Eyduran E (2007). Comparison of Some Pomological Characteristics of Blackberry Cultivars Growth in Ayaş Conditions. Ankara Universitesi Tarım Bilimleri Dergisi 13(1): 69-74
  • Akin M, Eyduran, SP & Eyduran E (2020). Analysis of macro nutrient related growth responses using multivariate adaptive regression splines. Plant Cell Tiss Organ Cult 140, 661–670. https://doi.org/10.1007/s11240-019-01763-8
  • Aksoy A, Erturk YE, Eyduran E and Tariq MM (2018). Comparing predictive performances of MARS and CHAID algorithms for defining factors affecting final fattening live weight in cultural beef cattle enterprises Pakistan Journal of Zoology. 50(6): 2279-2286
  • Ali M, Eyduran E, Tariq MM, Tirink C, Abbas F, Bajwa MA, Baloch MH, Nizamani AH, Waheed A, Awan MA, Shah SH, Ahmad Z and Jan S (2015). Comparison of artificial neural network and decision tree algorithms used for predicting live weight at post weaning period from some biometrical characteristics in Harnai sheep. Pakistan J. Zool., 47: 1579-1585
  • Anonim. Comparing different cattle breeds Use the 2016 across-breed EPD table. 2020; http://www.beefmagazine.com/ print/15608 (Erişim tarihi: 31.05.2020)
  • Aytekin I, Eyduran E, Koksal K, Akşahan R, Keskin I (2018). Prediction of fattening final liveweight from some body measurements and fattening period in young bulls of crossbred and exotic breeds using MARS data mining algorithm. Pakistan J. Zoology. 50(1): 189-195
  • Canga D & Boga M (2020). Determination of the Effect of Some Properties on Egg Yield with Regression Analysis Met-hod Bagging Mars and R Application. Turkish Journal of Agriculture - Food Science and Technology 8(8): 1705-1712
  • Canga D & Boga M (2019). Use of MARS in livestock and An application. III. International Scientific and Vocational Studies Congress – Science and Health Full Text Paper Book; Nevşehir, Turkey p. 31-37
  • Canga D, Yavuz E & Efe E (2019). Use of MARS Data Mining Algorithm for Egg Weight Estimation. The International Congress on Domestic Animal Breeding Genetics and Husbandry-19 Full Text Paper Book; Prague, Czechia; 2019.p.127
  • Celik S & Boydak E (2020). Descrıptıon of The Relatıonshıps Between Dıfferent Plant Characterıstıcs In Soybean Usıng Multıvarıate Adaptıve Regressıon Splınes Mars Algorıthm, Journal Of Animal And Plant Sciences. Vol. 30, No. 2, Pp. 431–441
  • Celik S, Eyduran E, Tatliyer A, Karadas K, Kara MK et al (2020) Comparing predictive performances of some nonlinear functions and Multivariate Adaptive Regression Splines (MARS) for describing the growth of Daera Dın Panah (DDP) goat in Pakistan. Pakistan J. Zoology 50(3): 1187-1190
  • Celik S & Yilmaz O (2018). Prediction of Body Weight of Turkish Tazi Dogs using Data Mining Techniques: Classification and Regression Tree (CART) and Multivariate Adaptive Reg-ression Splines (MARS). Pakistan Journal of Zoology 50(2). doi:10.17582/journal.pjz/2018.50.2.575.583
  • Chou SM, Lee TS, Shao YE, Chen IF (2004). Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Systems with Applications 27:133-142
  • Craven P &Wahba G (1979). Estimating the correct degree of smoothing by the method of generalized cross-validation. Numerische Mathematik 31: 377-403
  • Deconinck E, Xu QS, Put R, Coomans D, Massart DL, et al (2005). Prediction ofgastro-intestinal absorption using multivariate adaptive regression splines. Journal of Pharmaceutical and Biomedical Analysis; 39: 1021–1030
  • Devijver Pierre A.; Kittler, Josef (1982). Pattern Recognition: A Statistical Approach. London, GB: Prentice-Hall. ISBN 0-13-654236-0
  • Duru S & Sak H (2017). Fattening Performance and Carcass Characteristics of Simmental, Aberdeen Angus, Hereford, Limousine and Charolais Cattle Breeds in Turkey. Turkish Journal of Agriculture-Food Science and Technology. 5(11):1383-1388
  • Efe E, Bek Y & Şahin M (2000). Spss’te Çözümleri ile İstatistik Yöntemler II. Kahramanmaraş Sütçü İmam Üniversitesi Rektörlüğü. Yayın No: 73, Ders Kitapları Yayın No: 9, Turkey: Kahramanmaraş
  • Eyduran E, Akin M & Eyduran SP (2019). Application of multivariate adaptive regression splines in agricultural sciences through R Software. Nobel Bilimsel Eserler. Sertifika No:20779, Ankara ISBN: 978-605-2149-81-2
  • Eyduran E, Akkus O, Kara MK, Tırınk C, Tariq MM (2017a). Use of Multivariate Adaptive Regression Splines (Mars) in Predicting Body Weight from Body Measurements in Mengali Rams. International Conference on Agriculture, Food, Veterinary and Pharmacy Sciences, Cappadocia Turkey, 415
  • Eyduran E & Duman H (2020). R Yazilimi ile Multivariate Adaptive Regression Splines (Mars) Uygulamasi Ders Notlari Eyduran E & Gulbe A. ehaGoF: Calculates Goodness of Fit Statistics. R package version 0.1.0 2019. https://CRAN.R-project.org/package=ehaGoF
  • Eyduran E, Sevgenler H, Akın M, Eyduran SP (2018). Usage multivariate adaptive regression splines for predicting continuous responses. Animal and Plant Sciences. International Agricultural Science Congress Full Text Paper Book. Van, Turkey
  • Eyduran E, Tirink C, Karahan AE, Türkoğlu M, Tariq MM (2017b). Comparison of Predictive Performances of MARS and CART Algorithms through R Software. International Con-ference on Computational ans Statistical Methods in Applied Sciences, Samsun Turkey, 181
  • Friedman JH (1991). Multivariate Adaptive Regression Splines. Annls. Stat. 19: 1-141. https://doi. org/10.1214/aos/1176347963
  • Geisser, Seymour (1993). Predictive Inference. New York, NY: Chapman and Hall. ISBN 978-0-412-03471-8
  • Grzesiak W, Zaborski D, Sablik P, Żukiewicz A, Dybus A and Szatkowska I (2010). Detection of cows with insemination problems using selected classification models. Comput. Electron. Agric., 74: 265-273. https://doi.org/10.1016/j.compag.2010.09.001
  • Hastie T, Tibshirani R & Friedman J (2001). The Elements of Statistical Learning: Date mining, inference, and prediction. Springer. New York
  • Kayri, M (2010). The Analysis of Internet Addiction Scale Using Multivariate Adaptive Regression Splines, Iranian J Publ Health 2010; 39: 51-63
  • Kibet CE (2012). A Multıvarıate Adaptıve Regressıon Splınes Approach to Predıct the Treatment Outcomes of Tuberculosıs Patıents in Kenya, Science in Biometry to The University of Nairobi, Kenya
  • Kohavi R (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence. San Mateo, CA: Morgan Kaufmann. 2 (12): 1137–1143. CiteSeerX 10.1.1.48.529
  • Kor A & Ertuğrul M (2000) Canlı hayvanda karkas kompozisyonu tahmin yöntemleri. Hayvansal Üretim 41(1) Kumlu S (2000). Hayvancılık Örgütleri. Türkiye Damızlık Sığır Yetiştiricileri Merkez Birliği Yayınları. Yayın No: 2, Ankara
  • Milborrow S (2018). Milborrow. Derived from mda:mars by T. Hastie and R. Tibshirani. İnternet url: https://CRAN.R-project.org/package=earth (10.10.2018)
  • Milborrow S (2011). Derived from MDA: MARS by T. Hastie and Tibshirani earth: Multivariate adaptive regression splines, R package
  • Mukhopadhyay A & Iqbal A (2000). Prediction of mechanical property of steel strips using multivariate adaptive regression splines. Journal of Applied Statistics 36(1): 1-9.[ElectronicJournal],http://www.informaworld.com/smpp/title~content=t713428038.
  • Oğuz A (2014). Çok Değişkenli Uyarlanabilir Regresyon Zincirlerinin İrdelenmesi Ve Bir Uygulama Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Erzincan Üniversitesi, Erzincan Türkiye, 2014
  • Orhan H, Teke EÇ & Karcı Z (2018). Application of Multivariate Adaptive Regression Splines (MARS) for Modeling the Lactation Curves. Kahramanmaraş Sütçü İmam Üniversitesi Tarım ve Doğa Dergisi . 21(3):363-373. (in Turkish with an abstract in English) doi: 10.18016/ksudobil.334237
  • Put R, Xu QS, Massart DL, Vander Heyden (2004). Multivariate adaptive regression splines (MARS) in chromatographic quantitative structure–retention relationship studies. Journal of Chromatography A. 1055: 11–19
  • R Core Team (2014). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, http://www.R-project.org
  • Ripley BD (1996) Pattern Recognition and Neural Networks, Cambridge: Cambridge University Press, p. 354 Salford Systems (2001). MARSTM User Guide. Salford Systems, San Diego, CA, USA
  • Seker İ, Köseman A, Seker P, Baykalır Y (2017). Carcass Grading System Used in the United States for Beef Carcass Quality Evaluation. 15(2):192-203. (in Turkish with an abstract in English) doi: 10.24323/akademik-gida.333676
  • Sengul T, Celik S, Eyduran E, Iqbal F (2020). Predicting egg production in Chukar partridges using nonlinear models and multivariate adaptive regression splines (MARS) algorithm. European Poultry Science. 84
  • Sevgenler H (2019). Keçilere Ait Kimi Özelliklerin Canlı Ağırlık Üzerindeki Etkilerini Belirlemek Amacıyla Kullanılan Veri Madenciliği Algoritmalarının (Cart, Chaıd Ve Mars) Karşılaştırılması. Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Iğdır Üniversitesi, Iğdır, Türkiye
  • Sevimli, Y (2009). Çok değişkenli uyarlanabilir regresyon uzanımlarının bir split-mouth çalışmasında uygulaması Yüksek Lisans Tezi, Fen Bilimleri Enstitüsü, Marmara Üniversitesi, İstanbul, Türkiye
  • Tunay KB (2001). Türkiye’de paranın gelir dolaşım hızlarının MARS yöntemiyle tahmini, ODTÜ Gelişme Dergisi 2001; 28 (3-4): 431-454
  • Xu QS, Daeyaert F, Lewi PJ, Massart DL (2006). Studies of relationship between biological activities and HIV Reverse Transcriptase Inhibitors by Multivariate Adaptive Regression Splines with Curds and Whey. Chemometrics and Intelligent Laboratory Systems. 82: 24-30
  • Yıldız G (2020). Ankara Üniversitesi, Veteriner Fakültesi, Hayvan Besleme ve Beslenme Hastaliklari Anabilim Dalı; 2020. https://acikders.ankara.edu.tr/pluginfile.php/46643/mod_resource/content/0/BESI-SIGIRLARININ-BESLENMESI-GULTEKIN-YILDIZ.pdf
  • Zaborski D, Ali M, Eyduran E, Grzesiak W, Tariq MM, et al (2019). Prediction of Selected Reproductive Traits of Indigenous Harnai Sheep under the Farm Management System via various Data Mining Algorithms. Pakistan J. Zoo. 51(2):421-431. doi:10.17582/journal.pjz/2019.51.2.421.43
  • Zahrádková R, Bartoň L, Bureš D, Teslík V, Kudrna V (2010). Comparison of growth performance and slaughter characteristics of Limousine and Charolais heifers. Archiv Tierzucht; 2010. 53(5): 520-528
  • Zhang W& Goh AT (2016). Multivariate adaptive regression splines and neural network models for prediction of pile drivability. Geoscience Frontiers 7(1): 45-52
There are 49 citations in total.

Details

Primary Language English
Journal Section Makaleler
Authors

Demet Çanga 0000-0003-3319-7084

Publication Date April 25, 2022
Submission Date October 30, 2020
Acceptance Date May 17, 2021
Published in Issue Year 2022 Volume: 28 Issue: 2

Cite

APA Çanga, D. (2022). Use of MARS Data Mining Algorithm Based on Training and Test Sets in Determining Carcass Weight of Cattle in Different Breeds. Journal of Agricultural Sciences, 28(2), 259-268. https://doi.org/10.15832/ankutbd.818397

Journal of Agricultural Sciences is published open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).