Peper Jordan M J, Kalivas John H
Department of Chemistry, Idaho State University, Pocatello, Idaho, USA.
Appl Spectrosc. 2024 Sep 28:37028241280669. doi: 10.1177/00037028241280669.
Modern developments in autonomous chemometric machine learning technology strive to relinquish the need for human intervention. However, such algorithms developed and used in chemometric multivariate calibration and classification applications exclude crucial expert insight when difficult and safety-critical analysis situations arise, e.g., spectral-based medical decisions such as noninvasively determining if a biopsy is cancerous. The prediction accuracy and interpolation capabilities of autonomous methods for new samples depend on the quality and scope of their training (calibration) data. Specifically, analysis patterns within target data not captured by the training data will produce undesirable outcomes. Alternatively, using an immersive analytic approach allows insertion of human expert judgment at key machine learning algorithm junctures forming a sensemaking process performed in cooperation with a computer. The capacity of immersive virtual reality (IVR) environments to render human comprehensible three-dimensional space simulating real-world encounters, suggests its suitability as a hybrid immersive human-computer interface for data analysis tasks. Using IVR maximizes human senses to capitalize on our instinctual perception of the physical environment, thereby leveraging our innate ability to recognize patterns and visualize thresholds crucial to reducing erroneous outcomes. In this first use of IVR as an immersive analytic tool for spectral data, we examine an integrated IVR real-time model selection algorithm for a recent model updating method that adapts a model from the original calibration domain to predict samples from shifted target domains. Using near-infrared data, analyte prediction errors from IVR-selected models are reduced compared to errors using an established autonomous model selection approach. Results demonstrate the viability of IVR as a human data analysis interface for spectral data analysis including classification problems.
自主化学计量机器学习技术的现代发展致力于消除对人工干预的需求。然而，在化学计量多元校准和分类应用中开发和使用的此类算法，在出现困难且关乎安全的分析情况时，例如基于光谱的医疗决策，如非侵入性地确定活检是否为癌性时，会排除关键的专家见解。自主方法对新样本的预测准确性和插值能力取决于其训练（校准）数据的质量和范围。具体而言，训练数据未捕获的目标数据中的分析模式将产生不良结果。相比之下，使用沉浸式分析方法允许在关键的机器学习算法节点插入人类专家判断，形成与计算机协同执行的意义构建过程。沉浸式虚拟现实（IVR）环境能够呈现人类可理解的三维空间以模拟真实世界的情况，这表明它适合作为用于数据分析任务的混合沉浸式人机界面。使用IVR可最大限度地利用人类感官，利用我们对物理环境的本能感知，从而利用我们天生识别模式和可视化对减少错误结果至关重要的阈值的能力。在首次将IVR用作光谱数据的沉浸式分析工具时，我们研究了一种集成的IVR实时模型选择算法，用于一种最近的模型更新方法，该方法将原始校准域中的模型进行调整，以预测来自偏移目标域的样本。与使用既定的自主模型选择方法相比，使用近红外数据时，IVR选择的模型的分析物预测误差有所降低。结果证明了IVR作为光谱数据分析（包括分类问题）的人工数据分析界面的可行性。
