Using data augmentation, active learning, and visual analytics for learning with limited examples on mobility data sets

越来越多的人使用定位设备技术，如智能手机和支持GPS的相机，导致大量的移动数据被收集、存储和用于分析。然而，很难找到包含注释轨迹的数据集，因为标签往往表示其识别依赖于人类解释的特定行为。标签在监督学习问题中至关重要，可以预测结果，例如携带智能手机的用户使用的交通工具，或者船只是否正在进行捕鱼活动。了解移动对象的行为取决于多种因素，并且通常无法自动推断。因此，即使对于领域专家，注释过程也可能是复杂且耗时的，因为轨迹数据具有巨大的体积，并且在许多情况下，具有若干属性（高维度）。因此，减少标签工作是研究人员和海洋运输，旅游，野生动物监测和交通管理工作的行业感兴趣。 一个有前途的方法来解决缺乏标签是使用数据增强技术，创建新的数据样本从已知的模式，以扩大决策边界的分类模型。另一种方法是使用主动学习策略，帮助从未标记的数据池中选择一个子集的例子，以便获得注释将导致模型性能的最大提高。目前的策略不能直接使用的轨迹数据，由于其时空依赖性。轨迹数据往往是大的和高维的，使得难以从用户的角度对其进行注释。可以使用视觉分析来组装用于注释轨迹的交互式工具，以有效帮助用户在标记过程中处理所有上述方面。可视化分析使用交互式图形界面，通过将交互性与自动化可视化分析相结合，帮助更高效地做出决策。因此，该研究计划旨在开发新的数据增强，主动学习和可视化分析策略和工具，以使移动数据挖掘领域能够通过有限的示例来学习强大的模型。使用有限的示例集进行学习是机器学习中最具挑战性的设置之一。该计划将培养几名本科生和研究生，他们将从移动数据分析领域的现实和行业相关论文主题中受益。与这些主题合作的政府部门和加拿大行业将受益于该计划的成果（高素质人才（HQP），软件系统的生产和库）。此外，该计划将为当前涉及移动数据的应用科学和技术挑战提供新的解决方案，降低创建标记移动数据集的成本，并提供更准确的服务。

The increasing access to positioning devices technologies such as smartphones and GPS-enabled cameras has resulted in vast volumes of mobility data collected, stored, and available for analysis. However, it is difficult to find data sets containing annotated trajectories since labels tend to indicate a specific behavior whose identification depends on human interpretation. Labels are essential in supervised learning problems to predict an outcome, such as the transportation mean used by a user carrying a smartphone or if a vessel is performing a fishing activity. Understanding the moving object behavior depends on multiple factors and, often, cannot be automatically inferred. Therefore, the annotation process may be complex and time-consuming even for domain experts since trajectory data has a vast volume and, in many cases, several attributes (high dimensionality). Consequently, reducing the labeling effort is of interest to researchers and industries working in marine transportation, tourism, wildlife monitoring, and traffic management. A promising approach to address the lack of labels is to use data augmentation techniques that create new data samples from known patterns in order to expand the decision boundary of a classification model. Another approach is to use active learning strategies that help to select a subset of examples from an unlabeled pool of data so that obtaining annotations will result in a maximal increase of model performance. Current strategies cannot be used directly with trajectory data due to their spatiotemporal dependencies. Trajectory data tend to be large and high dimensional, making it difficult to annotate it from the user's perspective. Assembling interactive tools for annotating trajectories to effectively assist the user in coping with all the aforementioned aspects during the labeling process can be done using visual analytics. Visual analytics uses interactive graphical interfaces to help make decisions more efficiently and effectively by merging interactivity with automated visual analysis. Therefore, this research program aims to develop new data augmentation, active learning, and visual analytics strategies and tools to allow the mobility data mining field to learn robust models with a limited set of examples. Learning with a limited set of examples is one of the most challenging setups in machine learning. This program will train several undergraduate and graduate students who will benefit from realistic and industry-relevant thesis' topics in the mobility data analysis domain. Government departments and Canadian industries working with these topics will benefit from the outcomes of this program (Highly Qualified Personnel (HQP), production of software systems, and libraries). Moreover, this program will provide new solutions to current applied science and technology challenges involving mobility data, decrease costs involved in creating labeled mobility datasets, and provide more accurate services.