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Revista Brasileira de Zootecnia

On-line version ISSN 1806-9290

Abstract

CELIK, Senol; EYDURAN, Ecevit; KARADAS, Koksal  and  TARIQ, Mohammad Masood. Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan. R. Bras. Zootec. [online]. 2017, vol.46, n.11, pp.863-872. ISSN 1806-9290.  https://doi.org/10.1590/s1806-92902017001100005.

The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth) and testicular (testicular length, scrotal length, and scrotal circumference) measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1) and with interaction (MARS_2) terms. The superiority order in the predictive accuracy of the algorithms was found as CART > CHAID ≈ Exhaustive CHAID > MARS_2 > MARS_1 > RBF > MLP. Moreover, all tested algorithms provided a strong predictive accuracy for estimating body weight. However, MARS is the only algorithm that generated a prediction equation for body weight. Therefore, it is hoped that the available results might present a valuable contribution in terms of predicting body weight and describing the relationship between the body weight and body and testicular measurements in revealing breed standards and the conservation of indigenous gene sources for Mengali sheep breeding. Therefore, it will be possible to perform more profitable and productive sheep production. Use of data mining algorithms is useful for revealing the relationship between body weight and testicular traits in describing breed standards of Mengali sheep.

Keywords : ANN; artificial intelligence; data mining; decision tree; MARS algorithm.

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