Abstract
Internet of Things (IoT) devices generate heterogeneous data over time; and relying solely on individual data points is inadequate for accurate analysis. Segmentation is a common preprocessing step in many IoT applications, including IoT-based activity recognition, aiming to address the limitations of individual events and streamline the process. However, this step introduces at least two families of uncontrollable biases. The first is caused by the changes made by the segmentation process on the initial problem space, such as dividing the input data into 60 seconds windows. The second category of biases results from the segmentation process itself, including the fixation of the segmentation method and its parameters. To address these biases, we propose to redefine the segmentation problem as a special case of a decomposition problem, including three key components: a decomposer, resolutions, and a composer. The inclusion of the composer task in the segmentation process facilitates an assessment of the relationship between the original problem and the problem after the segmentation. Therefore, It leads to an improvement in the evaluation process and, consequently, in the selection of the appropriate segmentation method. Then, we formally introduce our novel meta-decomposition or learning-to-decompose approach. It reduces the segmentation biases by considering the segmentation as a hyperparameter to be optimized by the outer learning problem. Therefore, meta-decomposition improves the overall system performance by dynamically selecting the appropriate segmentation method without including the mentioned biases. Extensive experiments on four real-world datasets demonstrate the effectiveness of our proposal.
Abstract (translated)
物联网设备随着时间的推移会产生异构数据；仅依赖个体数据点进行准确分析是不够的。在许多物联网应用中，包括基于物联网的活动识别，分段是一个常见的预处理步骤，旨在解决个体事件的局限性，并简化过程。然而，这一步骤引入了至少两个不可控的偏见家族。第一个偏见是由分段过程对初始问题空间所做的改变引起的，例如将输入数据分为60秒窗口。第二个偏见是由分段过程本身引起的，包括对分段方法和其参数的固定。为解决这些偏见，我们提出将分段问题重新定义为分解问题的一般情况，包括三个关键组件：分解者、分辨率和支持者。将支持者任务包含在分段过程中有助于评估分割前后原始问题与问题之间的关系。因此，这导致了评估过程的改进，并相应地选择了适当的分段方法。接着，我们正式引入了我们新颖的元分解或学习分解方法。它通过将分段视为外学习问题中的超参数来减少分段偏见。因此，元分解通过动态选择适当的分段方法来提高整体系统性能，而不会包括上述偏见。在四个真实世界数据集上的大量实验证明了我们建议的有效性。
URL
https://arxiv.org/abs/2404.11742
