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Provedor de dados:  R. Bras. Zootec.
País:  Brazil
Título:  Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
Autores:  Celik,Senol
Eyduran,Ecevit
Karadas,Koksal
Tariq,Mohammad Masood
Data:  2017-11-01
Ano:  2017
Palavras-chave:  ANN
Artificial intelligence
Data mining
Decision tree
MARS algorithm
Resumo:  ABSTRACT 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.
Tipo:  Info:eu-repo/semantics/article
Idioma:  Inglês
Identificador:  http://www.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017001100863
Editor:  Sociedade Brasileira de Zootecnia
Relação:  10.1590/s1806-92902017001100005
Formato:  text/html
Fonte:  Revista Brasileira de Zootecnia v.46 n.11 2017
Direitos:  info:eu-repo/semantics/openAccess
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