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: 1537898
• 负责人: George Runger
• 金额: $ 17.5万
• 依托单位: Arizona State University
• 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 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1537898&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 proposed 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.

在制造业、医疗保健或能源生产等众多行业中，当前的传感器技术可以以低成本产生大量的对象测量值。每个测量都由几个相互关联的变量组成，目标是使用数据构建计算机模型，允许预测对象的类别（例如患者的健康状况或制造零件的质量）。沿着传感器数据，需要一些对象的类标签来训练计算机模型。虽然传感器变量可以频繁地快速且廉价地获得（例如，医学图像或化学分析），与每个对象相关联的类别标签可能需要耗时且昂贵的人力。因此，应注意选择对构建预测性计算机模型信息量最大的对象进行标记。通常，人们迭代地选择对象，其中来自先前选择的批次的类标签引导下一批要标记的对象。这就是所谓的主动学习策略。本研究的目的是寻找新的主动学习方法，加速模型的构建，并在大数据集的属性测量系统中提供更好的预测。现有的主动学习方法通常基于对联合输入/输出分布的强假设或使用基于距离的方法。这些方法容易受到输入空间中的噪声的影响，仅假设数值输入，并且通常在高维中工作得很差。在应用中，数据集通常是大的，噪声，包含缺失值和混合变量类型。在这项研究中，提出了一种非参数的主动学习问题的方法来解决这些挑战。该算法是基于一个批量多样化的策略，适用于一个合奏的决策树。还将考虑一种新型的主动学习策略，该策略考虑未标记数据所在的流形的几何结构。数据空间的几何属性可以产生更多信息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英特尔公司在机器学习和优化设计方面互补专业知识的合作成果。英特尔的参与将有助于确保成果的成功传播和广泛适用性。