Predicting the metabolizable energy content of corn for ducks: a comparison of support vector regression with other methods
Abstract
Support vector regression (SVR) is used in this study to develop models to estimate apparent metabolizable energy (AME), AME corrected for nitrogen (AMEn), true metabolizable energy (TME), and TME corrected for nitrogen (TMEn) contents of corn fed to ducks based on its chemical composition. Performance of the SVR models was assessed by comparing their results with those of artificial neural network (ANN) and multiple linear regression (MLR) models. The input variables to estimate metabolizable energy content (MJ kg-1) of corn were crude protein, ether extract, crude fibre, and ash (g kg-1). Goodness of fit of the models was examined using R2, mean square error, and bias. Based on these indices, the predictive performance of the SVR, ANN, and MLR models was acceptable. Comparison of models indicated that performance of SVR (in terms of R2) on the full data set (0.937 for AME, 0.954 for AMEn, 0.860 for TME, and 0.937 for TMEn) was better than that of ANN (0.907 for AME, 0.922 for AMEn, 0.744 for TME, and 0.920 for TMEn) and MLR (0.887 for AME, 0.903 for AMEn, 0.704 for TME, and 0.902 for TMEn). Similar findings were observed with the calibration and testing data sets. These results suggest SVR models are a promising tool for modelling the relationship between chemical composition and metabolizable energy of feedstuffs for poultry. Although from the present results the application of SVR models seems encouraging, the use of such models in other areas of animal nutrition needs to be evaluated.Downloads
References
Ahmadi H, Golian A, Mottaghitalab M, Narimanzadeh N, 2008. Prediction model for true metabolizable energy of feather meal and poultry offal meal using group method of data handling-type neural network. Poultry Sci 87: 1909-1912. http://dx.doi.org/10.3382/ps.2007-00507 PMid:18753461
Chatterjee S, Hadi AS, Price B, 2000. Regression analysis by example, 3rd ed. John Wiley & Sons Inc., NY, USA.
Cheng CS, Chen PW, Huang, KK, 2011. Estimating the shift size in the process mean with support vector regression and neural networks. Expert Syst Appl 38: 10624-10630. http://dx.doi.org/10.1016/j.eswa.2011.02.121
Cortes C, Vapnik V, 1995. Support vector networks. Mach Learn 20: 273-297. http://dx.doi.org/10.1007/BF00994018
Cristianini N, Shawe-Taylor J, 2000. Introduction to support vector machines and other kernel-based learning methods. Cambridge Univ. Press, Cambridge, UK. http://dx.doi.org/10.1017/CBO9780511801389 PMCid:PMC26651
Drucker H, Burges CJC, Kaufman L, Smola A, Vapnik V, 1997. Support vector regression machines. In: Advances in neural information processing systems, vol. 9 (Mozer MC, Jordan MI, Petsche T, eds.). MIT Press, Cambridge, MA, USA, pp: 155-161.
Faridi A, Mottaghitalab M, Ahmadi A, 2012a. Sensitivity analysis of an early egg production predictive model in broiler breeders based on dietary nutrient intake. J Agric Sci 150: 87-93. http://dx.doi.org/10.1017/S0021859611000402
Faridi A, Golian A, Ahmadi A, 2012b. Comparison of responses to dietary protein and lysine in broiler chicks reared before and after 2000 via neural network models. J Agric Sci 150: 775-786. http://dx.doi.org/10.1017/S0021859612000305
Kara Y, Boyacioglu MA, Baykan ÖK, 2011. Predicting direction of stock price index movement using artificial neural networks and support vector machines: the sample of the Istanbul Stock Exchange. Expert Syst Appl 38: 5311-5319. http://dx.doi.org/10.1016/j.eswa.2010.10.027
Kecman V, 2005. Support vector machines – an introduction. In: Support vector machines: theory and applications (Wang L, ed.). Springer-Verlag, Berlin-Heidelberg-NY, pp: 1-48.
Lahiri SK, Ghanta KC, 2008. Prediction of pressure drop of slurry flow in pipeline by hybrid support vector regression and genetic algorithm model. Chinese J Chem Eng 16: 841-848. http://dx.doi.org/10.1016/S1004-9541(09)60003-3
Leeson S, Yersin A, Volker L, 1993. Nutritive value of the 1992 corn crop. J Appl Poultry Res 2: 208-213.
Mantero P, Moser G, Serpico SB, 2005. Partially supervised classification of remote sensing images through SVM-based probability density estimation. IEEE T Geosci Remote Sens 43: 559-570. http://dx.doi.org/10.1109/TGRS.2004.842022
Nandi S, Badhe Y, Lonari J, Sridevi U, Rao BS, Tambe SS, Kulkarni BD, 2004. Hybrid process modeling and optimization strategies integrating neural networks/support vector regression and genetic algorithms: study of benzene isopropylation on Hbeta catalyst. Chem Eng J 97: 115-129. http://dx.doi.org/10.1016/S1385-8947(03)00150-5
SAS, 2003. SAS/STAT software, version 9. SAS Institute Inc., Cary, NC, USA.
Scholkopf B, Smola AJ, 2002. Learning with kernels. MIT Press, Boston, MA, USA.
Sedghi M, Ebadi MR, Golian A, Ahmadi H, 2011. Estimation and modeling true metabolizable energy of sorghum grain for poultry. Poultry Sci 90: 1138-1143. http://dx.doi.org/10.3382/ps.2010-01005 PMid:21489965
StatSoft, 2009. Statistica data analysis software system, vers 7.1. StatSoft Inc., Tulsa, OK, USA.
Vapnik VN, 1995. The nature of statistical learning theory. Springer, NY, USA. http://dx.doi.org/10.1007/978-1-4757-2440-0 PMid:8555380
Vapnik VN, Golowich S, Smola A, 1997. Support vector method for function approximation, regression estimation and signal processing. In: Advance in neural information processing system, vol. 9 (Mozer M, Jordan M, Petsche, T. eds.). MIT Press, Cambridge, MA, USA, pp: 281-287.
Witten IH, Frank E, Hall MA, 2011. Data mining, practical machine learning tools and techniques, 3rd ed. Morgan Kaufmann, San Francisco, CA, USA.
Yang CC, Shieh MD, 2010. A support vector regression based prediction model of affective responses for product form design. Comput Ind Eng 59: 682-689. http://dx.doi.org/10.1016/j.cie.2010.07.019
Zhao F, Zhang HF, Hou SS, Zhang ZY, 2008. Predicting metabolizable energy of normal corn from its chemical composition in adult Pekin ducks. Poultry Sci 87: 1603-1608. http://dx.doi.org/10.3382/ps.2007-00494 PMid:18648055
Zhou Z, Wan HF, Li Y, Chen W, Qi ZL, Peng P, Peng J, 2010. The influence of the amylopectin/amylose ratio in samples of corn on the true metabolizable energy value for ducks. Anim Feed Sci Technol 157: 99-103. http://dx.doi.org/10.1016/j.anifeedsci.2010.02.005
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