气候变化领域集成服务门户(CCPortal): An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers

CCPortal

DOI	10.1186/s40462-021-00245-x
	An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers
	Yu, Hui; Deng, Jian; Nathan, Ran; Kroeschel, Max; Pekarsky, Sasha; Li, Guozheng; Klaassen, Marcel
通讯作者	Li, GZ (通讯作者)，Druid Technol Co Ltd, Chengdu, Sichuan, Peoples R China. ; Li, GZ (通讯作者)，Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou, Gansu, Peoples R China.
发表日期	2021
ISSN	2051-3933
卷号	9 期号:1
英文摘要	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), support vector machine (SVM), artificial neural network (ANN), random forest (RF) and extreme gradient boosting (XGBoost) to classify behaviour using ACC data from three bird species (white stork Ciconia ciconia, griffon vulture Gyps fulvus and common crane Grus grus) and two mammals (dairy cow Bos taurus and roe deer Capreolus capreolus). Results Using a range of quality criteria, SVM, ANN, RF and XGBoost performed well in determining behaviour from ACC data and their good performance appeared little affected when greatly reducing the number of input features for model training. On-board runtime and storage-requirement tests showed that notably ANN, RF and XGBoost would make suitable on-board classifiers. Conclusions Our identification of using feature reduction in combination with ANN, RF and XGBoost as suitable methods for on-board behavioural classification of continuous ACC data has considerable potential to benefit movement ecology and behavioural research, wildlife conservation and livestock husbandry.
英文关键词	Accelerometer; Behaviour classification; On-board processing; ANN; Random forest; XGBoost
语种	英语
WOS研究方向	Environmental Sciences & Ecology
WOS类目	Ecology
WOS记录号	WOS:000635118600001
来源期刊	MOVEMENT ECOLOGY
来源机构	中国科学院西北生态环境资源研究院
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/254686
作者单位	[Yu, Hui; Klaassen, Marcel] Deakin Univ, Sch Life & Environm Sci, Ctr Integrat Ecol, Geelong, Vic, Australia; [Yu, Hui; Deng, Jian; Li, Guozheng] Druid Technol Co Ltd, Chengdu, Sichuan, Peoples R China; [Nathan, Ran; Pekarsky, Sasha] Hebrew Univ Jerusalem, Alexander Silberman Inst Life Sci, Dept Evolut Systemat & Ecol, Movement Ecol Lab, Jerusalem, Israel; [Kroeschel, Max] Forest Res Inst Baden Wurttemberg, Dept Wildlife Ecol, Freiburg, Germany; [Kroeschel, Max] Univ Freiburg, Chair Wildlife Ecol & Wildlife Management, D-79106 Freiburg, Germany; [Li, Guozheng] Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Lanzhou, Gansu, Peoples R China
推荐引用方式 GB/T 7714	Yu, Hui,Deng, Jian,Nathan, Ran,et al. An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers[J]. 中国科学院西北生态环境资源研究院,2021,9(1).
APA	Yu, Hui.,Deng, Jian.,Nathan, Ran.,Kroeschel, Max.,Pekarsky, Sasha.,...&Klaassen, Marcel.(2021).An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers.MOVEMENT ECOLOGY,9(1).
MLA	Yu, Hui,et al."An evaluation of machine learning classifiers for next-generation, continuous-ethogram smart trackers".MOVEMENT ECOLOGY 9.1(2021).