Predicting Slaughter Weight in Pigs with Regression Tree Ensembles

A. Alsahaf, G. Azzopardi, B. Ducro, R. F. Veerkamp, N. Petkov

OnderzoeksoutputAcademicpeer review

8 Citaten (Scopus)
26 Downloads (Pure)


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.

Originele taal-2English
TitelApplications of Intelligent Systems
SubtitelProceedings of the 1st International APPIS Conference 2018
RedacteurenNicolai Petkov, Nicola Strisciuglio, Carlos M. Travieso-Gonzalez
UitgeverijIOS Press
Aantal pagina's9
ISBN van elektronische versie978-1-61499-929-4
ISBN van geprinte versie978-1-61499-928-7
StatusPublished - 1-jan.-2018
Evenement1st International Conference on Applications of Intelligent Systems, APPIS 2018 - Las Palmas de Gran Canaria, Spain
Duur: 10-jan.-201812-jan.-2018

Publicatie series

NaamFrontiers in Artificial Intelligence and Applications
ISSN van geprinte versie0922-6389


Conference1st International Conference on Applications of Intelligent Systems, APPIS 2018
StadLas Palmas de Gran Canaria

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