Predicting Slaughter Weight in Pigs with Regression Tree Ensembles

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

8 Citations (Scopus)
30 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.

Original languageEnglish
Title of host publicationApplications of Intelligent Systems
Subtitle of host publicationProceedings of the 1st International APPIS Conference 2018
EditorsNicolai Petkov, Nicola Strisciuglio, Carlos M. Travieso-Gonzalez
PublisherIOS Press
Number of pages9
ISBN (Electronic)978-1-61499-929-4
ISBN (Print)978-1-61499-928-7
Publication statusPublished - 1-Jan-2018
Event1st International Conference on Applications of Intelligent Systems, APPIS 2018 - Las Palmas de Gran Canaria, Spain
Duration: 10-Jan-201812-Jan-2018

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


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


  • Animal production
  • Ensemble learning
  • Gradient boosting
  • Pigs
  • Random forest
  • XGBoost


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