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

在制造业、医疗保健或能源生产等众多行业中，目前的传感器技术可以以低成本对一个物体进行大量测量。每次测量都由几个相互关联的变量实例组成，目标是利用这些数据建立一个计算机模型，使人们能够预测对象的类别（例如患者的健康状况或制造部件的质量）。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快速而廉价地获得（例如，医学图像或化学分析），但与每个对象相关的类别标签可能需要人力，既耗时又昂贵。因此，应该小心选择对构建预测计算机模型最有信息的对象进行标记。通常是迭代地选择对象，其中以前选择的批处理中的类标签引导下一批对象进行标记。这就是所谓的主动学习策略的目的。本研究的目的是寻找新的主动学习方法，加速模型构建，并在具有大量属性测量数据集的系统中提供更好的预测。这将导致更有效和更有生产力的系统，这将有利于美国经济和社会。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噪声的影响，只假设数值输入，并且在高维情况下通常效果不佳。在应用程序中，数据集通常很大，有噪声，包含缺失值和混合变量类型。在本研究中，提出了一种非参数的主动学习方法来解决这些挑战。该算法基于一种应用于决策树集合的批量多样化策略。本文还将考虑一种新的主动学习策略，该策略考虑了未标记数据所在流形的几何结构。数据空间的几何特性可能会产生更多信息丰富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的互补专业知识的合作成果。英特尔公司的参与将有助于确保研究结果的成功传播和广泛适用。

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.