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

从数据中构建预测模型的监督式机器学习方法传统上依赖于标记样本。然而，在许多现实世界的应用中，样本要么没有标记，要么标记不精确，也就是说，标签经常是模糊的、冲突的或不完整的。这就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)使用不同专业知识的多个标签器来注释样本；(2)带注释的标签与一组样本相关联，而不是与单个样本相关联；(3)通过对多个专家评价进行建模，得到注释。该项目引入了一个通用的双凸规划、极大极小优化和基于多目标优化的框架，用于从不精确标记的数据中学习预测模型。所产生的算法将在许多实际应用中进行评估。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。该项目为研究生和本科生在机器学习及其应用方面的课程开发和基于研究的高级培训提供了更多的机会。项目产生的算法的开源软件实现的传播也有助于项目更广泛的影响。有关该项目的更多信息可以在http://www.labhealthinfo.uconn.edu/home/MachineLearning.jsp上找到

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