An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers.

ABSTRACT:

Background

Our understanding of movement patterns and behaviours of wildlife has advanced greatly through the use of improved tracking technologies, including application of accelerometry (ACC) across a wide range of taxa. However, most ACC studies either use intermittent sampling that hinders continuity or continuous data logging relying on tracker retrieval for data downloading which is not applicable for long term study. To allow long-term, fine-scale behavioural research, we evaluated a range of machine learning methods for their suitability for continuous on-board classification of ACC data into behaviour categories prior to data transmission.

Methods

We tested six supervised machine learning methods, including linear discriminant analysis (LDA), decision tree (DT), sup

SUBMITTER: Yu H 

PROVIDER: S-EPMC8011142 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature