X-LIBS：利用可解释人工智能和激光诱导击穿光谱进行可解释的土壤分类

中文摘要 AI 辅助

机器学习（ML）已成为利用激光诱导击穿光谱（LIBS）进行土壤分析的强大工具。然而，传统的黑箱模型往往缺乏可解释性，限制了其在决策过程中的有效性。本研究引入了可解释人工智能（XAI）技术，以增强基于LIBS的土壤分类中偏最小二乘判别分析（PLS-DA）模型的可解释性和分类性能。采用局部可解释模型无关解释（LIME）来识别与土壤元素相关的局部光谱特征，并解释这些特征对分类结果的贡献。为了提高未标记光谱的分类性能，通过依次移除LIME识别为对分类至关重要的顶部局部光谱特征来量化预测不确定性。较高的不确定性与改变标签所需移除的特征数量较少相关。对于在标签改变前必须移除大量特征的光谱，被视为高置信度预测，并纳入协同训练过程。针对光谱模糊和稳定类别，分别定义了翻转计数的阈值，以有效管理不确定性识别。为解决PLS-DA对类别不平衡的敏感性，协同训练中包含了来自每个类别的等比例伪标签。所提出的方法在公开可用的EMSLIBS数据集上进行了评估，测试准确率达到92.69%，与文献中表现最佳的方法相当。XAI结果提供了对误分类原因的见解，并识别了对准确预测至关重要的主导光谱特征，推进了基于LIBS的土壤分析的可解释AI解决方案。

英文摘要

Machine learning (ML) has emerged as a powerful tool for soil analysis using Laser-Induced Breakdown Spectroscopy (LIBS). However, traditional black-box models often lack interpretability, limiting their effectiveness in decision-making processes. This study introduced explainable AI (XAI) techniques to enhance both the interpretability and classification performance of Partial Least Squares Discriminant Analysis (PLS-DA) models for soil classification with LIBS. Local Interpretable Model-agnostic Explanations (LIME) was employed to identify the local spectral features associated with soil elements, providing explanations for their contributions to classification outcomes. To improve classification performance on unlabeled spectra, prediction uncertainty was quantified by sequentially removing the top local spectral features identified by LIME as critical to the classification. Higher uncertainty was associated with a smaller number of feature removals needed to change the label. Spectra for which a large number of features had to be removed before the label changed were treated as high-confidence predictions and admitted to a co-training process. Thresholds on this flip count were defined separately for spectrally ambiguous and stable classes to manage uncertainty identification effectively. To address the sensitivity of PLS-DA to class imbalances, equal proportions of pseudo-labels from each class were included in co-training. The proposed method was evaluated on the publicly available EMSLIBS dataset, achieving a test accuracy of 92.69\%, competitive with the best-performing methods in the literature. The XAI results provided insights into the causes of misclassifications and identified the dominant spectral features critical for accurate predictions, advancing interpretable AI solutions for LIBS-based soil analysis.

发表机构

• Faculty of Digital Innovation, Arts and Sciences, Saskatchewan Polytechnic（萨斯喀彻温理工大学数字创新、艺术与科学学院）

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