干旱区生态环境知识资源中心(Arid): Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework

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DOI	10.1016/j.geoderma.2023.116604
	Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework
	Bogaert, Patrick; Taghizadeh-Mehrjardi, Ruhollah; Hamzehpour, Nikou
通讯作者	Bogaert, P
来源期刊	GEODERMA
ISSN	0016-7061
EISSN	1872-6259
出版年	2023
卷号	437
英文摘要	In the digital soil mapping framework, machine learning (ML) algorithms are currently the most popular meth-ods for the spatial prediction of soil properties. The fast developments of easy-to-use software implementations for a large panel of ML algorithms have encouraged comparison studies between algorithms, with the goal of ranking their performances and identifying the best ones among them. However, as no firm conclusions can be drawn about the best ML algorithm to be used in general, this suggests that combining a set of them could be a better approach. Numerous methods have been proposed to do so, most of them relying on a linear weighting of the individual algorithms. However, there are almost as many methods for linearly weighting ML algorithms as there are ML algorithms, thus leaving the problem unsolved. Moreover, these weighting methods are mostly used out-of-the-box, without paying a proper attention to the associated hypotheses. In this paper, we propose to address this issue by setting the problem in a more formal framework. Starting from classical hypotheses, it is shown how the benefit of averaging various ML algorithms can be estimated from their joint performances. Relying afterwards on the most commonly used linear weighting schemes, it is reminded that, as long as the performance metrics are based on mean square errors, the best averaging method is by essence the best linear (unbiased) predictor. Using a more general Bayesian framework, it is also shown that accounting for conditional biases when weighting ML algorithms is a key issue for obtaining improved predictions, and explicit formulas are proposed for that goal. Finally, these theoretical results are illustrated and discussed using a soil data set collected over an arid and semi-arid region in Iran where clay content, calcium carbonate equivalent, soil organic carbon and electrical conductivity were measured in topsoil samples.
英文关键词	Best linear predictor BLUP Data fusion Soil mapping Urmia Lake
类型	Article
语种	英语
开放获取类型	hybrid
收录类别	SCI-E
WOS记录号	WOS:001047023800001
WOS关键词	PREDICTION ; TEXTURE
WOS类目	Soil Science
WOS研究方向	Agriculture
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/396696
推荐引用方式 GB/T 7714	Bogaert, Patrick,Taghizadeh-Mehrjardi, Ruhollah,Hamzehpour, Nikou. Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework[J],2023,437.
APA	Bogaert, Patrick,Taghizadeh-Mehrjardi, Ruhollah,&Hamzehpour, Nikou.(2023).Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework.GEODERMA,437.
MLA	Bogaert, Patrick,et al."Model averaging of machine learning algorithms for digital soil mapping: A minimum variance framework".GEODERMA 437(2023).