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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

基本信息

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

项目摘要

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.

在化学系化学测量与成像计划的支持下，以及刺激竞争研究计划(EPSCoR)和数学科学部的部分共同资助下，特拉华大学的卡尔·布克什和乔斯林·阿尔坎塔拉·加西亚教授以及俄克拉荷马州立大学的巴里·拉文教授正在合作提高手持化学传感器快速样品分类的能力。例如，当两个或两个以上感兴趣的类别之间的观察到的差异与样本之间的自然差异或单个样本上的重复测量之间的差异相比很小时，这个问题对于与化学取证和ART保护有关的现场分析来说是重要的。该小组正在开发先进的统计和数学工具，以便能够定量确定可用来评估推论可靠性的统计可信度。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。参加这项跨学科研究的研究生和本科生获得了高级数据分析的技能。这个项目是一个合作项目，旨在研究现代测量科学中化学建模的重要基础问题：(1)通过变量选择提高分类模型的效率，(2)为考虑误差和类别成员关系的非正态分布的分类分配稳健的置信度，(3)当来自不同传感器的信息可用时，提高分类模型的可靠性。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。该项目探讨了变量选择、数据融合、仪器参数优化和分类模型性能之间的关系。目标包括类分布和/或测量误差不是正态分布的真实世界分类应用。采用嵌套Bootstraps和遗传算法来解决这一多层优化问题。所开发的方法将根据需要进行修改，以用于基于最小二乘法、人工神经网络和知识近邻的分类。结果数据集将公之于众。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。