论文标题
部分可观测时空混沌系统的无模型预测
Easy Ensemble: Simple Deep Ensemble Learning for Sensor-Based Human Activity Recognition
论文作者
论文摘要
基于传感器的人类活动识别（HAR）是物联网服务中的最重要技术。 HAR使用表示形式学习，该学习会自动从原始数据中学习功能表示形式，这是主流方法，因为很难将来自原始传感器数据的相关信息解释为设计有意义的功能。合奏学习是提高概括性能的强大方法。但是，深度合奏学习需要各种程序，例如数据分配和培训多个模型，这些模型耗时且计算昂贵。在这项研究中，我们为HAR提出了简单的合奏（EE），这使得可以轻松地在单个模型中实施深层整体学习。此外，我们提出输入蒙版，作为使EE输入多样化的一种方法。与常规的集合学习方法相比，在基准数据集上进行HAR的实验证明了EE和输入掩盖的有效性及其特征。
Sensor-based human activity recognition (HAR) is a paramount technology in the Internet of Things services. HAR using representation learning, which automatically learns a feature representation from raw data, is the mainstream method because it is difficult to interpret relevant information from raw sensor data to design meaningful features. Ensemble learning is a robust approach to improve generalization performance; however, deep ensemble learning requires various procedures, such as data partitioning and training multiple models, which are time-consuming and computationally expensive. In this study, we propose Easy Ensemble (EE) for HAR, which enables the easy implementation of deep ensemble learning in a single model. In addition, we propose input masking as a method for diversifying the input for EE. Experiments on a benchmark dataset for HAR demonstrated the effectiveness of EE and input masking and their characteristics compared with conventional ensemble learning methods.
