用沉浸式分析重新定义频谱数据分析:探索领域偏移模型空间,优化模型选择。

IF 2.2 3区 化学 Q2 INSTRUMENTS & INSTRUMENTATION Applied Spectroscopy Pub Date : 2024-09-28 DOI:10.1177/00037028241280669
Jordan M J Peper, John H Kalivas
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引用次数: 0

摘要

自主化学计量学机器学习技术的现代发展致力于放弃对人工干预的需求。然而,当出现困难和对安全至关重要的分析情况时,例如基于光谱的医疗决策(如无创确定活检是否为癌症),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就会排除关键的专家洞察力。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。具体来说,如果目标数据中的分析模式没有被训练数据捕获,就会产生不理想的结果。另外,使用沉浸式分析方法可以在机器学习算法的关键节点插入人类专家的判断,形成一个与计算机合作执行的感知决策过程。沉浸式虚拟现实(IVR)环境能够模拟现实世界的遭遇,呈现人类可理解的三维空间,这表明它适合作为数据分析任务的混合沉浸式人机界面。使用 IVR 可以最大限度地利用人体感官对物理环境的本能感知,从而利用我们与生俱来的识别模式和可视化阈值的能力,这对减少错误结果至关重要。在这个首次将 IVR 用作光谱数据沉浸式分析工具的项目中,我们研究了一种集成 IVR 实时模型选择算法,该算法适用于一种最新的模型更新方法,该方法可调整原始校准域的模型,以预测移动目标域的样本。利用近红外数据,IVR 选择模型的分析物预测误差比使用既定自主模型选择方法的误差要小。结果表明,IVR 作为光谱数据分析(包括分类问题)的人类数据分析界面是可行的。
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Redefining Spectral Data Analysis with Immersive Analytics: Exploring Domain-Shifted Model Spaces for Optimal Model Selection.

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.

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来源期刊
Applied Spectroscopy
Applied Spectroscopy 工程技术-光谱学
CiteScore
6.60
自引率
5.70%
发文量
139
审稿时长
3.5 months
期刊介绍: Applied Spectroscopy is one of the world''s leading spectroscopy journals, publishing high-quality peer-reviewed articles, both fundamental and applied, covering all aspects of spectroscopy. Established in 1951, the journal is owned by the Society for Applied Spectroscopy and is published monthly. The journal is dedicated to fulfilling the mission of the Society to “…advance and disseminate knowledge and information concerning the art and science of spectroscopy and other allied sciences.”
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