Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan

Detalhes bibliográficos
Autor(a) principal: Celik,Senol
Data de Publicação: 2017
Outros Autores: Eyduran,Ecevit, Karadas,Koksal, Tariq,Mohammad Masood
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Zootecnia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017001100863
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.
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spelling Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of PakistanANNartificial intelligencedata miningdecision treeMARS algorithmABSTRACT 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.Sociedade Brasileira de Zootecnia2017-11-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017001100863Revista Brasileira de Zootecnia v.46 n.11 2017reponame:Revista Brasileira de Zootecnia (Online)instname:Sociedade Brasileira de Zootecnia (SBZ)instacron:SBZ10.1590/s1806-92902017001100005info:eu-repo/semantics/openAccessCelik,SenolEyduran,EcevitKaradas,KoksalTariq,Mohammad Masoodeng2018-01-03T00:00:00Zoai:scielo:S1516-35982017001100863Revistahttps://www.rbz.org.br/pt-br/https://old.scielo.br/oai/scielo-oai.php||bz@sbz.org.br|| secretariarbz@sbz.org.br1806-92901516-3598opendoar:2018-01-03T00:00Revista Brasileira de Zootecnia (Online) - Sociedade Brasileira de Zootecnia (SBZ)false
dc.title.none.fl_str_mv Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
title Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
spellingShingle Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
Celik,Senol
ANN
artificial intelligence
data mining
decision tree
MARS algorithm
title_short Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
title_full Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
title_fullStr Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
title_full_unstemmed Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
title_sort Comparison of predictive performance of data mining algorithms in predicting body weight in Mengali rams of Pakistan
author Celik,Senol
author_facet Celik,Senol
Eyduran,Ecevit
Karadas,Koksal
Tariq,Mohammad Masood
author_role author
author2 Eyduran,Ecevit
Karadas,Koksal
Tariq,Mohammad Masood
author2_role author
author
author
dc.contributor.author.fl_str_mv Celik,Senol
Eyduran,Ecevit
Karadas,Koksal
Tariq,Mohammad Masood
dc.subject.por.fl_str_mv ANN
artificial intelligence
data mining
decision tree
MARS algorithm
topic ANN
artificial intelligence
data mining
decision tree
MARS algorithm
description 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.
publishDate 2017
dc.date.none.fl_str_mv 2017-11-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017001100863
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-35982017001100863
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/s1806-92902017001100005
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Zootecnia
publisher.none.fl_str_mv Sociedade Brasileira de Zootecnia
dc.source.none.fl_str_mv Revista Brasileira de Zootecnia v.46 n.11 2017
reponame:Revista Brasileira de Zootecnia (Online)
instname:Sociedade Brasileira de Zootecnia (SBZ)
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reponame_str Revista Brasileira de Zootecnia (Online)
collection Revista Brasileira de Zootecnia (Online)
repository.name.fl_str_mv Revista Brasileira de Zootecnia (Online) - Sociedade Brasileira de Zootecnia (SBZ)
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