Challenges of machine learning model validation using correlated behaviour data: Evaluation of cross-validation strategies and accuracy measures.

ABSTRACT: Automated monitoring of the movements and behaviour of animals is a valuable research tool. Recently, machine learning tools were applied to many species to classify units of behaviour. For the monitoring of wild species, collecting enough data for training models might be problematic, thus we examine how machine learning models trained on one species can be applied to another closely related species with similar behavioural conformation. We contrast two ways to calculate accuracies, termed here as overall and threshold accuracy, because the field has yet to define solid standards for reporting and measuring classification performances. We measure 21 dogs and 7 wolves, and find that overall accuracies are between 51 and 60% for classifying 8 behaviours (lay, sit, stand, walk, trot, run, ea

SUBMITTER: Ferdinandy B 

PROVIDER: S-EPMC7371169 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature