Leveraging machine learning and accelerometry to classify animal behaviors with uncertainty
Animal-worn sensors, especially accelerometers, are increasingly used with machine-learning models to identify animal behaviors. These tools often struggle with uneven training data, uncertain predictions, and noisy results. To address these issues, Dr. Rafiq and Dr. Abrahms, with their collaborators, developed an open-source method that combines machine learning and statistical techniques to improve behavior classification and to provide “prediction sets,” which […]
