Figure 8. ACC data visualization including behaviour classification results using a dynamic graph. White stork ACC data is shown from segment 55 to 80 (cf Fig. 2b). Vertical black, dashed lines separate different observed behaviour types, while vertical grey dotted lines identify segments that have been incorrectly classified.
Conclusions
As demonstrated, the rabc package aims to assist researchers in developing good animal behaviour classification models in an interactive fashion. As human brains are extremely good at recognizing patterns, the visualization of data and results can greatly assist the workflow towards developing a behaviour classification model. Raw ACC data visualization assists in the detection of aberrant associated behaviour scores. Feature visualization helps researchers to understand how different features distribute across behaviours and whether the current behaviour set potentially needs adjustments, either by combining or by splitting behaviours up. Finally, classification-result visualization assists the understanding of misclassification patterns. Other than the visualization functionalities, this package provides complete functions to perform behaviour classification through XGboost, including: feature calculation, feature selection, model hyperparameter tuning, model training and validation and an output classifier for future ACC data classification.
Given its unique aim and functionality the rabc package will be a valuable addition to the growing array of R packages already available for behaviour and movement analyses (Joo et al., 2020). It is worth noting that the features calculated in this package can be further extended if deemed neccessary. Users can develop additional features and include these in the here described analyses and the ultimate generation of a behaviour classification model. Although we only use XGboost as the supervised machine learning model in this package, users can potentially use the output from the rabc package as input to the “caret” package. This will allow for the use of other machine learning models in generating behaviour classification models such as for example decision tree, support vector machine and random forest.