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

D3SC：协作研究：克服接近确定极限的分类挑战

• 批准号: 2003839
• 负责人: Karl Booksh
• 金额: $ 50万
• 依托单位: University of Delaware
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003839&HistoricalAwards=false
• 关键词: D3SC; Collaborative; Research; Overcoming; Challenges; D3SC; Collaborative; Research; Overcoming; Challenges

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.

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. 例如，对于与化学取证和艺术保护相关的现场分析，当观察到的两种或多种兴趣类别之间的差异与样品之间的自然变化相比或单个样本上的重复测量之间的差异很小时，问题很重要。 该团队正在开发先进的统计和数学工具，以实现可以评估推论的可靠性的统计信心的定量确定。该项目需要结合两个或多个不同传感器的信息，以提高整体性能。研究生和本科生参加了高级数据分析中的跨学科研究获得技能。 这些技能的需求很高。该项目是一项合作的努力，旨在调查现代测量科学中对化学建模重要的基本问题：（1）通过可变选择提高分类模型效率，（2）将强大的置信度分配给分类，以说明错误和班级成员的非正态分布分布和阶级的可靠性，（3）在各种传感器上增加构图的可靠性。主要的测量工具是手持激光诱导的分解光谱（LIBS）和X射线荧光（XRF）数据。该项目探讨了变量选择，数据融合，仪器参数的优化以及分类模型的性能之间的连接。目标包括实际分类应用程序，其中类别分布和/或测量错误未正态分布。正在使用嵌套的引导程序和遗传算法来解决此多层优化问题。 PLS-DA，ANN-和KNN驱动分类将根据需要修改开发的方法。最终的数据集将公开可用。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评论标准来评估的值得支持的。