Theses and Dissertations
Issuing Body
Mississippi State University
Advisor
Street, Garrett M.
Committee Member
Strickland, Bronson K.
Committee Member
Borger, Luca
Date of Degree
5-3-2019
Document Type
Graduate Thesis - Open Access
Major
Wildlife, Fisheries, and Aquaculture
Degree Name
Master of Science
College
College of Agriculture and Life Sciences
Department
Department of Wildlife, Fisheries and Aquaculture
Abstract
Studies of animal spatial distributions typically use prior knowledge of animal habitat requirements and behavioral ecology to deduce the most likely explanations of observed habitat use. Animal-borne accelerometers can be used to distinguish behaviors which allows us to incorporate in situ behavior into our understanding of spatial distributions. Past research has focused on using supervised machine-learning, which requires a priori specification of behavior to identify signals whereas unsupervised approaches allow the model to identify as many signal types as permitted by the data. The following framework couples direct observation to behavioral clusters identified from unsupervised machine learning on a large accelerometry dataset. A behavioral profile was constructed to describe the proportion of behaviors observed per cluster and the framework was applied to an acceleration dataset collected from wild pigs (Sus scrofa). Although, most clusters represented combinations of behaviors, a leave-p-out validation procedure indicated this classification system accurately predicted new data.
URI
https://hdl.handle.net/11668/20971
Recommended Citation
Dentinger, Jane Elizabeth, "An Unsupervised Machine-Learning Framework for Behavioral Classification from Animal-Borne Accelerometers" (2019). Theses and Dissertations. 621.
https://scholarsjunction.msstate.edu/td/621