How does extreme point sampling affect non-extreme simulation in geographical random forest?
How does extreme point sampling affect non-extreme simulation in geographical random forest?
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极值点采样如何影响地理随机森林中的非极值模拟？
DOI：
10.1007/s12145-024-01268-9
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发表时间：
2024
影响因子：
2.8
通讯作者：
Que, Xiang
中科院分区：
文献类型：
--
作者：
Wang, Hui;Chen, Meixu;Wang, Zhe;Huang, Li;Caudill, Christopher C.;Qu, Shijin;Que, Xiang
在建模过程中，空间异构性给训练数据集带来了大量的不确定性。任意选择训练样本可能会导致有偏差的模拟。虽然以前的研究提供了通过同质分区减少空间差异程度的机会，但关于分区配置对培训的影响的详细信息仍不清楚。此外，很少有研究研究极端抽样对非极端模拟的交叉影响。因此，我们扩展了先前的研究，以考察交叉影响，并进一步检验在使用空间分层抽样时，极高(Exh)和极低(Exl)分位数的划分是否同样对模拟偏差有贡献。统计评估表明，极端训练样本的选择确实会影响非极端模拟。当选择最小比例(25%)的EXH和EXL进行训练时，模型的性能最佳(均方根：2.735，VE：7.481，偏移量：-0.033)。进一步的分析还表明，执行部门和执行部门对这一进程的贡献是不平等的。特别是，非极端模拟对EXH训练数据更敏感，变化率更大，为0.043。这项研究为机器学习过程中的极值点采样提供了一个关键的见解。分区的不同敏感性要求在地理随机林中应用分层抽样时，极端训练样本应按百分比而不是按其数量进行调整。
Spatial heterogeneity brings numerous uncertainties to training datasets in the modeling process. An arbitrary selection of training samples can result in a biased simulation. Although previous research provides a chance of reducing the degree of spatial variance through homogeneous divisions, detailed information regarding the impact of the configuration of divisions for training remains unknown. Moreover, few studies investigate the cross impact of extreme sampling on non-extreme simulation. Therefore, we extend previous research to investigate the cross impact and further examine whether the divisions of extremely high (EXH) and low (EXL) quantiles contribute equally to the simulation bias when employing the spatial stratified sampling. Statistical assessment demonstrates that the selection of extreme training sample does affect the non-extreme simulation. The model has the best performance (RMSE: 2.735, VE: 7.481, Bias: -0.033) when the least proportion (25%) of EXH and EXL was selected for training. Further analysis also indicated that the EXH and EXL divisions contribute unequally to the process. Particularly, the non-extreme simulation is more sensitive to the EXH training data with a steeper change rate of 0.043. This research provides a critical insight into the extreme point sampling for a machine learning process. Different sensitivity of division calls upon that extreme training sample should be adjusted on a basis of percentage rather than their amounts when applying stratified sampling in Geographical Random Forest.