D3SC: Collaborative Research: Overcoming Challenges in Classification Near the Limit of Determination

在化学系化学测量成像项目的支持下，以及刺激竞争性研究的既定项目（EPSCoR）和数学科学系的部分共同资助下，特拉华州大学的Karl Booksh和Jocelyn Alcantara-Garcia教授以及俄克拉荷马州州立大学的巴里拉文教授正在合作提高手持式化学传感器快速样本分类的能力。& 例如，对于与化学法医学和艺术品保护相关的现场分析，当观察到的两个或更多个感兴趣类别之间的差异与样品之间或单个样品的重复测量之间的自然变化相比很小时，该问题很重要。 该小组正在开发先进的统计和数学工具，以便能够定量确定统计置信度，从而可以评估推断的可靠性。该项目需要结合来自两个或更多不同传感器的信息，以提高整体性能。参与这项跨学科研究的研究生和本科生将获得高级数据分析技能。 这些技能是非常高的需求。这个项目是一个合作的努力，旨在调查重要的基本问题，化学建模在现代测量科学：（1）通过变量选择提高分类模型效率，（2）将稳健的置信水平分配给说明错误和类成员的非正态分布的分类，（3）当来自不同传感器的信息可用时，增加分类模型的可靠性。主要的测量工具是手持式激光诱导击穿光谱（LIBS）和X射线荧光（XRF）数据。本计画探讨变量选择、资料融合、仪器参数最佳化与分类模式效能间的关系。目标包括类别分布和/或测量误差不是正态分布的真实世界分类应用。嵌套引导程序和遗传算法被用来解决这个多层优化问题。所开发的方法将根据需要修改PLS-DA，ANN和KNN驱动的分类。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

With support from the Chemical Measurement & Imaging program in the Division of Chemistry, and partial co-funding from the Established Program to Stimulate Competitive Research (EPSCoR) and the Division of Mathematical Sciences, Professors Karl Booksh and Jocelyn Alcantara-Garcia at the University of Delaware, and Professor Barry Lavine at Oklahoma State University, are collaborating to improve the ability of hand-held chemical sensors for rapid sample classification. The problem is important, for example, for field analysis related to chemical forensics and art conservation, when the observed differences between two or more classes of interest are small compared to the natural variation among samples or among replicate measurements on a single sample. The team is developing advanced statistical and mathematical tools to enable quantitative determination of the statistical confidence with which the reliability of inferences can be assessed. The project entails combining information from two or more disparate sensors in order improve overall performance. Graduate and undergraduate students participating in this interdisciplinary research gain skills in advanced data analysis. These skills are in very high demand.This project is a collaborative effort aimed at investigating fundamental issues important to chemical modeling in modern measurement science: (1) improving classification model efficiency through variable selection, (2) assigning robust confidence levels to classifications that account for non-normal distributions of errors and class memberships, (3) increasing reliability of classification models when information from different sensors is available. The primary measurement tools are hand-held Laser Induced Breakdown Spectroscopy (LIBS) and X-Ray Fluorescence (XRF) data. This project probes the connections among variable selection, data fusion, optimization of instrumental parameters, and the performance of classification models. Targets include real-world classification applications where the class distribution and/or the measurement errors are not normally distributed. Nested bootstraps and genetic algorithms are being employed to solve this multilayered optimization problem. The developed methods will be modified as needed for PLS-DA, ANN-, and KNN-driven classifications. Resulting data sets will be made publicly available.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.