The role of individual variability on the predictive performance of machine learning applied to large bio-logging datasets.

ABSTRACT: Animal-borne tagging (bio-logging) generates large and complex datasets. In particular, accelerometer tags, which provide information on behaviour and energy expenditure of wild animals, produce high-resolution multi-dimensional data, and can be challenging to analyse. We tested the performance of commonly used artificial intelligence tools on datasets of increasing volume and dimensionality. By collecting bio-logging data across several sampling seasons, datasets are inherently characterized by inter-individual variability. Such information should be considered when predicting behaviour. We integrated both unsupervised and supervised machine learning approaches to predict behaviours in two penguin species. The classified behaviours obtained from the unsupervised approach Expectation Maxim

SUBMITTER: Chimienti M 

PROVIDER: S-EPMC9672113 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature