2.4 Behaviors classification
We utilized the XGBoost algorithm, a boosting ensemble algorithm that
efficiently implements the Gradient boosting decision tree algorithm
(Van Soest, 2018), to classify sheep behaviors about energy expenditure
and foraging strategies. Specifically, we classified behaviors into
grazing (feeding, walking-feeding, walking) and nongrazing behaviors
(standing, lying, ruminating-standing, ruminating-lying), as the ODBA
was more accurately related to the active status of animals. During the
grazing period, actual observed behaviors were conducted for 3-5 days
every month, and we eliminated data with multiple behaviors or
behavioral changes within 1 minute to ensure the monotonicity of
behavioral data (Wang et al., 2020). Ultimately, 2500 individual
behavior segments (each segment lasting more than 30 s) were included
for analysis.
We acquired 27 features from motion sensors using the ’rabc’ package (Yu
and Klaassen, 2021), including mean, variance, standard deviation, max,
min, range, and ODBA for each ACC axis separately (denoted with prefixx, y, z in the output data frame), except for ODBA. After
filtration, we used 70% of the data combined with actual observed
behavior data to develop the behaviors classification model based on the
XGBoost algorithm. The remaining 30% was used to validate the
classification filter and report the classification accuracy. The
results showed more than 90% accuracy for behavior classification
(Supplementary Fig. 1a). Similarly, using only ODBA as a classification
criterion, we were able to accurately classify feeding behavior
(> 0.1 g) and non-feeding behavior (< 0.1 g)
(Supplementary Fig. 1b).