TY - GEN
T1 - Predicting Slaughter Weight in Pigs with Regression Tree Ensembles
AU - Alsahaf, A.
AU - Azzopardi, G.
AU - Ducro, B.
AU - Veerkamp, R. F.
AU - Petkov, N.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.
AB - Domestic pigs vary in the age at which they reach slaughter weight even under the controlled conditions of modern pig farming. Early and accurate estimates of when a pig will reach slaughter weight can lead to logistic efficiency in farms. In this study, we compare four methods in predicting the age at which a pig reaches slaughter weight (120 kg). Namely, we compare the following regression tree-based ensemble methods: random forest (RF), extremely randomized trees (ET), gradient boosted machines (GBM), and XGBoost. Data from 32979 pigs is used, comprising a combination of phenotypic features and estimated breeding values (EBV). We found that the boosting ensemble methods, GBM and XGBoost, achieve lower prediction errors than the parallel ensembles methods, RF and ET. On the other hand, RF and ET have fewer parameters to tune, and perform adequately well with default parameter settings.
KW - Animal production
KW - Ensemble learning
KW - Gradient boosting
KW - Pigs
KW - Random forest
KW - XGBoost
UR - http://www.scopus.com/inward/record.url?scp=85059621672&partnerID=8YFLogxK
U2 - 10.3233/978-1-61499-929-4-1
DO - 10.3233/978-1-61499-929-4-1
M3 - Conference contribution
AN - SCOPUS:85059621672
T3 - Frontiers in Artificial Intelligence and Applications
SP - 1
EP - 9
BT - Applications of Intelligent Systems - Proceedings of the 1st International APPIS Conference 2018, APPIS 2018
A2 - Petkov, Nicolai
A2 - Strisciuglio, Nicola
A2 - Travieso-Gonzalez, Carlos M.
PB - IOS Press
T2 - 1st International Conference on Applications of Intelligent Systems, APPIS 2018
Y2 - 10 January 2018 through 12 January 2018
ER -