Collaborative Research: Active Statistical Learning: Ensembles, Manifolds, and Optimal Experimental Design

协作研究：主动统计学习：集成、流形和最优实验设计

• 批准号: 1537987
• 负责人: Enrique Del Castillo
• 金额: $ 17.5万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537987&HistoricalAwards=false
• 关键词: Collaborative; Research; Active; Statistical; Learning

In numerous industries such as manufacturing, health care or energy production, current sensor technology can generate enormous quantities of measurements of an object at low cost. Each measurement consists of several instances of interrelated variables, and the goal is to use the data to build a computer model that permits one to predict the class of an object (such as the health condition of a patient or the quality of a manufactured part). Along with the sensor data, the class labels for some objects are needed to train the computer model. While the sensor variables can frequently be obtained rapidly and inexpensively (e.g., medical images or chemical analyses) the class label associated with each object might require human effort that is time-consuming and expensive. Therefore, care should be taken to select the objects to label that are most informative for building the predictive computer model. Often one selects objects iteratively, where the class labels from the previously selected batch guides the next batch of objects to label. This is the purpose of a so-called active learning strategy. The purpose of this research is to find new active learning methods that accelerate model building and provide better predictions in systems where large datasets of attribute measurements are available. This will result in more efficient and productive systems that will benefit the U.S. economy and society.Existing active learning methods are often based on strong assumptions for the joint input/output distribution or use a distance-based approach. These methods are susceptible to noise in the input space, assume numerical inputs only, and often work poorly in high dimensions. In applications, data sets are often large, noisy, contain missing values and mixed variable types. In this research, a non-parametric approach to the active learning problem is planned to address these challenges. The algorithm is based on a batch diversification strategy applied to an ensemble of decision trees. A novel active learning strategy that considers the geometric structure of the manifold where the unlabeled data resides will also be considered. The geometric properties of the data space may result in more informative active learning solutions. This is a collaborative effort between Arizona State University, Pennsylvania State University, and Intel Corporation with complementary expertise in machine learning and optimal design. The participation of Intel will help ensure the successful dissemination and broad applicability of the results.

在诸如制造，医疗保健或能源生产之类的众多行业中，当前的传感器技术可以以低成本产生大量对象的测量。每个测量值包括几个相互关联的变量的实例，目的是使用数据来构建一个允许人们预测对象类别的计算机模型（例如，患者的健康状况或制成部分的质量）。除了传感器数据外，还需要一些对象的类标签来训练计算机模型。虽然传感器变量通常可以迅速，廉价地获得（例如，医学图像或化学分析），但与每个对象相关的类标签可能需要耗时且昂贵的人类努力。因此，应注意选择对构建预测计算机模型最有用的标签对象。通常，一个从先前选择的批次指南标记的类标记的对象经常选择对象迭代。这是所谓的主动学习策略的目的。这项研究的目的是找到新的活跃学习方法，这些方法可以加速模型构建，并在可用的大量属性测量数据集中提供更好的预测。这将导致更高效，更有生产力的系统，从而使我们的经济和社会受益。存在活跃的学习方法通​​常基于对联合输入/输出分配或使用基于距离的方法的强有力的假设。这些方法容易受到输入空间中噪声的影响，仅假设数值输入，并且在高维度上通常工作较差。在应用程序中，数据集通常很大，嘈杂，包含缺失值和混合变量类型。在这项研究中，计划针对积极学习问题的非参数方法来应对这些挑战。该算法基于应用于决策树集合的批处理多元化策略。一项新型的主动学习策略，考虑了未标记数据所在的流形的几何结构。数据空间的几何特性可能会导致更有用的主动学习解决方案。这是亚利桑那州立大学，宾夕法尼亚州立大学和Intel Corporation之间的合作努力，具有机器学习和最佳设计方面的互补专业知识。英特尔的参与将有助于确保成功的传播和广泛的结果。