将回归转为分类，提升数据少时的土壤属性预测不确定性估计
An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric Applications
- 把土壤属性预测从回归转为分类任务，实现模型无关的不确定性估计
- 在德国两个农田数据集上表现优于传统方法，提升不确定性可靠性
- 适合数据稀缺的土壤建模研究者使用，尤其适用于机器学习新手
本文提出一种模型无关的方法，用于提升土壤属性预测中的不确定性估计，这对推进土壤科学和数字土壤制图至关重要。针对土壤研究中常见的数据稀缺问题，我们改进了不确定性估计技术，通过将回归任务转化为分类问题，不仅可生成可靠的不确定性评估，还能应用现有表现优异但尚未在土壤建模中使用的机器学习算法。基于德国两个农业田块采集的数据集进行的实证研究表明，该方法具有实际应用价值。结果表明，所提方法在不确定性估计方面优于当前常用的模型。
原文摘要 · Abstract (English)
This paper introduces a model-agnostic approach designed to enhance uncertainty estimation in the predictive modeling of soil properties, a crucial factor for advancing pedometrics and the practice of digital soil mapping. For addressing the typical challenge of data scarcity in soil studies, we present an improved technique for uncertainty estimation. This method is based on the transformation of regression tasks into classification problems, which not only allows for the production of reliable uncertainty estimates but also enables the application of established machine learning algorithms with competitive performance that have not yet been utilized in pedometrics. Empirical results from datasets collected from two German agricultural fields showcase the practical application of the proposed methodology. Our results and findings suggest that the proposed approach has the potential to provide better uncertainty estimation than the models commonly used in pedometrics.
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