RT Journal Article
T1 A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach
A1 Divina, Federico
A1 García Torres, Miguel
A1 Gómez-Vela, Francisco Antonio
A1 Rodríguez Baena, Domingo Savio
K1 Animal behavior prediction
K1 Machine learning
K1 Forecasting methods
K1 Ensemble learning
AB Automatic determination of abnormal animal activities can be helpful for the timely detection of signs of health and welfare problems. Usually, this problem is addressed as a classification problem, which typically requires manual annotation of behaviors. This manual annotation can introduce noise into the data and may not always be possible. This motivated us to address the problem as a time-series forecasting problem in which the activity of an animal can be predicted. In this work, different machine learning techniques were tested to obtain activity patterns for Iberian pigs. In particular, we propose a novel stacking ensemble learning approach that combines base learners with meta-learners to obtain the final predictive model. Results confirm the superior performance of the proposed method relative to the other tested strategies. We also explored the possibility of using predictive models trained on an animal to predict the activity of different animals on the same farm. As expected, the predictive performance degrades in this case, but it remains acceptable. The proposed method could be integrated into a monitoring system that may have the potential to transform the way farm animals are monitored, improving their health and welfare conditions, for example, by allowing the early detection of a possible health problem.
PB AIMS PRESS
YR 2024
FD 2024-04-11
LK https://hdl.handle.net/10433/22511
UL https://hdl.handle.net/10433/22511
LA en
NO Federico Divina, Miguel García-Torres, Francisco Gómez-Vela, Domingo S. Rodriguez-Baena. A stacking ensemble learning for Iberian pigs activity prediction: a time series forecasting approach[J]. AIMS Mathematics, 2024, 9(5): 13358-13384. doi: 10.3934/math.2024652
NO FECYT -- APRENDIZAJE PROFUNDO Y APRENDIZAJE ONLINE EXPLICABLES PARA SOST...PY20-00870UPO-138516contract 2015/00111/001
NO Universidad Pablo de Olavide de Sevilla
DS RIO
RD May 9, 2026