Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
Date
2017Author
Celik, Senol and Eyduran, Ecevit and Karadas, Koksal and Tariq, Mohammad
Masood
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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
approximate to 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.
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