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A hierarchical classification method used to classify livestock behaviour from sensor data

Suparwito, H., Wong, K.W., Xie, H., Rai, S. and Thomas, D. (2019) A hierarchical classification method used to classify livestock behaviour from sensor data. Lecture Notes in Computer Science, 11909 . pp. 204-215.

Link to Published Version: https://doi.org/10.1007/978-3-030-33709-4_18
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Abstract

One of the fundamental tasks in the management of livestock is to understand their behaviour and use this information to increase livestock productivity and welfare. Developing new and improved methods to classify livestock behaviour based on their daily activities can greatly improve livestock management. In this paper, we propose the use of a hierarchical machine learning method to classify livestock behaviours. We first classify the livestock behaviours into two main behavioural categories. Each of the two categories is then broken down at the next level into more specific behavioural categories. We have tested the proposed methodology using two commonly used classifiers, Random Forest, Support Vector Machine and a newer approach involving Deep Belief Networks. Our results show that the proposed hierarchical classification technique works better than the conventional approach. The experimental studies also show that Deep Belief Networks perform better than the Random Forest and Support Vector Machine for most cases.

Item Type: Journal Article
Murdoch Affiliation: Information Technology, Mathematics and Statistics
Publisher: Springer Verlag
Copyright: © 2019 Springer Nature Switzerland AG
Other Information: Conference paper from International Conference on Multi-disciplinary Trends in Artificial Intelligence (MIWAI) 2019; Kuala Lumpur, Malaysia. 17 - 19 November 2019
URI: http://researchrepository.murdoch.edu.au/id/eprint/54073
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