GV: EAGER: Innovative Analysis and Visualization Approaches for Understanding Model Uncertainty

GV：EAGER：理解模型不确定性的创新分析和可视化方法

• 批准号: 1050168
• 负责人: Marie desJardins
• 金额: $ 10.3万
• 依托单位: University of Maryland Baltimore County
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-01 至 2014-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1050168&HistoricalAwards=false
• 关键词: GV; EAGER; Innovative; Analysis; Visualization

This exploratory project strives to develop a new approach to support human understanding of the uncertainty that is inherent in the structure and predictions of complex models.Specifically, the focus in the project is on understanding several types of uncertainty that are associated with model predictions.Sample uncertainty occurs when regions of the instance space are not well represented in the training data, and predictions are therefore based on sparse information. Model instability occurs when model predictions vary, depending on the training data that was used to construct the model. Prediction variability occurs when a given observation may have noisy attributes, and this input uncertainty leads to uncertainty in the model's predictions. Novel analytical techniques are developed to create meta-models that characterize these three forms of uncertainty. To facilitate user understanding of the nature and distribution of these multiple types of uncertainty across the model space, novel visualization methods represent these meta-models in a display space. Finally, a novel evaluation methodology is used to measure whether, and in what ways, important characteristics of the meta-models are captured in the visualization display space.This work develops novel techniques in the fields of machine learning and data visualization. Contributions in machine learning include more powerful methods for constructing and analyzing meta-models that characterize multiple types of uncertainty associated with predictive models. Data visualization research focuses on new approaches for representing multi-valued, probabilistic, and complex data, enabling the display of the nature and range of model predictions and uncertainty. An interdisciplinary contribution is the development of a novel methodology for evaluating the quality of model visualizations with respect to the preservation of important model and meta-model characteristics.The broader impacts of this project may be grouped into three major clusters: a new model building paradigm; fostering scientific collaboration; and integrating research and education. The results are expected to provide foundations for further research is management of uncertainty in deriving models representing a wide range of phenomena. This project lays a technical groundwork that can contribute to new collaborations between the PIs and application domain experts, facilitating broad interdisciplinary collaborations. Project results will be widely disseminated via the project web site (http://maple.cs.umbc.edu/complexmodels/). Finally, through teaching and training activities, this research project is also well suited to include the introduction of undergraduates to the possibilities of research and the incorporation of project topics into the PIs' courses on visualization and artificial intelligence.

这个探索性项目致力于开发一种新的方法来支持人类理解复杂模型的结构和预测中固有的不确定性。具体来说，该项目的重点是理解与模型预测相关的几种类型的不确定性。当实例空间的区域在训练数据中没有很好地表示时，因此预测是基于稀疏信息的。当模型预测发生变化时，会发生模型不稳定性，这取决于用于构建模型的训练数据。当给定的观测可能具有噪声属性时，会发生预测可变性，并且这种输入不确定性会导致模型预测的不确定性。开发新的分析技术来创建表征这三种形式的不确定性的元模型。为了便于用户理解跨模型空间的这些多种类型的不确定性的性质和分布，新颖的可视化方法在显示空间中表示这些元模型。最后，一种新的评估方法被用来衡量是否，以及以何种方式，元模型的重要特征被捕获在可视化显示空间。这项工作开发了机器学习和数据可视化领域的新技术。机器学习的贡献包括用于构建和分析元模型的更强大的方法，这些元模型表征了与预测模型相关的多种类型的不确定性。数据可视化研究的重点是表示多值、概率和复杂数据的新方法，从而能够显示模型预测和不确定性的性质和范围。一个跨学科的贡献是开发一种新的方法，用于评估模型可视化的质量，保留重要的模型和元模型characteristics.The更广泛的影响，这个项目可以分为三个主要集群：一个新的模型建设范式，促进科学合作，并整合研究和教育。预计这些结果将为进一步的研究提供基础，即在推导代表各种现象的模型时管理不确定性。该项目奠定了技术基础，有助于PI和应用领域专家之间的新合作，促进广泛的跨学科合作。项目成果将通过项目网站（http：//maple.cs.umbc.edu/complexmodels/）广泛传播。最后，通过教学和培训活动，该研究项目也非常适合包括向本科生介绍研究的可能性，并将项目主题纳入PI的可视化和人工智能课程。