Abstract
Estimating animal’s genetic merit (or breeding value) plays a major role in the Manchego sheep selection scheme (ESROM), started fifteen years ago with the goal of improving Manchego sheep production figures. In the ESROM scheme the breeding value is estimated each semester by using BLUP animal model. In this paper we study the use of data mining techniques to deal with breeding value classification. The purpose of the paper is not to replace the use of BLUP in the ESROM, on the contrary, we intend to learn in a supervised way from the results produced by BLUP, and to use the learned models to provide preliminary information about the breeding value of an animal. We use standard classification techniques combined with feature subset selection in order to identify good (subsets of) predictors. We also show that the classifiers accuracy can be considerably improved by attribute construction.
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Flores, M.J., Gámez, J.A. (2005). Breeding Value Classification in Manchego Sheep: A Study of Attribute Selection and Construction. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2005. Lecture Notes in Computer Science(), vol 3682. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552451_185
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DOI: https://doi.org/10.1007/11552451_185
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28895-4
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