论文标题
基于反馈的动态功能选择，用于约束连续数据采集
Feedback-Based Dynamic Feature Selection for Constrained Continuous Data Acquisition
论文作者
论文摘要
相关和高质量的数据对于成功开发机器学习应用程序至关重要。对于配备大量传感器（例如连接的车辆和机器人）的动态系统上的机器学习应用程序，如何以有效的方式找到相关和高质量的数据功能是一个具有挑战性的问题。在这项工作中，我们解决了在约束的连续数据采集中选择特征选择的问题。我们提出了一种基于反馈的动态特征选择算法，该算法有效地以逐步的方式从动态系统中从动态系统中进行了数据集。我们将顺序特征选择过程作为马尔可夫决策过程。带有探索组件的机器学习模型性能反馈用作$ε$ - 绿色动作选择中的奖励功能。我们的评估表明，所提出的基于反馈的特征选择算法比约束基线方法具有优越的性能，并且与不受约束的基线方法相匹配的性能。
Relevant and high-quality data are critical to successful development of machine learning applications. For machine learning applications on dynamic systems equipped with a large number of sensors, such as connected vehicles and robots, how to find relevant and high-quality data features in an efficient way is a challenging problem. In this work, we address the problem of feature selection in constrained continuous data acquisition. We propose a feedback-based dynamic feature selection algorithm that efficiently decides on the feature set for data collection from a dynamic system in a step-wise manner. We formulate the sequential feature selection procedure as a Markov Decision Process. The machine learning model performance feedback with an exploration component is used as the reward function in an $ε$-greedy action selection. Our evaluation shows that the proposed feedback-based feature selection algorithm has superior performance over constrained baseline methods and matching performance with unconstrained baseline methods.
