III: Small: Is Imprecise Supervision Useful? Leveraging Ambiguous, Incomplete or Conflicting Data Annotations

三：小：监管不严有用吗？

• 批准号: 1320586
• 负责人: Jinbo Bi
• 金额: $ 33.74万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1320586&HistoricalAwards=false
• 关键词: III; Small; Imprecise; Supervision; Useful

Supervised machine learning approaches to building predictive models from data traditionally rely on labeled samples. However, in many real-world applications, samples are either unlabeled or labeled imprecisely labeled, i.e., labels are often ambiguous, conflicting, or incomplete. This presents the problem of learning predictive models under imprecise supervision.This project aims to develop effective algorithms to address three different scenarios that lead to imprecise supervision (1) multiple labelers with varying expertise are employed to annotate samples; (2) annotated labels are associated with a set of samples instead of an individual sample; (3) annotations are derived by modeling multiple expert assessments. The project introduces a general bi-convex programming, minimax optimization, and multi-objective optimization based framework for learning predictive models from imprecisely labeled data. The resulting algorithms will be evaluated on a number of real-world applications. Broader Impacts: The results of this research are likely to impact a range of biomedical applications, including medical image labeling, longitudinal behavioral studies, genomics, and drug safety. The project offers enhanced opportunities for curriculum development and research-based advanced training of grauduate amd undergraduate students in machine learning and its applications. Dissemination of open source software implementation of algorithms resulting from the project also contribute to the project's broader impact. Additional information about the project can be found at: http://www.labhealthinfo.uconn.edu/home/MachineLearning.jsp

传统上，从数据中构建预测模型的监督机器学习方法依赖于标记样本。然而，在许多现实世界的应用中，样本要么是未标记的，要么是不精确标记的，即，标签通常是模糊的、冲突的或不完整的。这提出了在不精确监督下学习预测模型的问题。该项目旨在开发有效的算法来解决导致不精确监督的三种不同情况：（1）使用具有不同专业知识的多个标签来注释样本;（2）注释标签与一组样本而不是单个样本相关联;（3）通过对多个专家评估进行建模来获得注释。该项目介绍了一个通用的双凸规划，极大极小优化和多目标优化的框架，用于从不精确标记的数据中学习预测模型。由此产生的算法将在一些现实世界的应用程序进行评估。更广泛的影响：这项研究的结果可能会影响一系列生物医学应用，包括医学图像标记，纵向行为研究，基因组学和药物安全。该项目为机器学习及其应用的研究生和本科生的课程开发和基于研究的高级培训提供了更好的机会。传播开源软件实施该项目所产生的算法也有助于该项目产生更广泛的影响。有关该项目的更多信息，请访问：http://www.labhealthinfo.uconn.edu/home/MachineLearning.jsp