论文标题
在线汽车：在线学习的自适应汽车框架
Online AutoML: An adaptive AutoML framework for online learning
论文作者
论文摘要
自动化机器学习（AUTOML）已成功地用于假定学习任务是静态的设置。但是，在许多实际情况下，数据分布会随着时间的推移而发展，并且尚待显示汽车技术是否可以在动态环境中有效设计在线管道。这项研究旨在自动化在线学习的管道设计，同时不断适应数据漂移。为此，我们设计了一种自适应在线自动化机器学习（OAML）系统，搜索在线学习者的完整管道配置空间，包括预处理算法和融合技术。该系统将在线学习者的固有适应能力与自动化管道（RE）优化功能相结合。为了专注于可以适应不断发展的目标的优化技术，我们评估了异步遗传编程和连续的半速度，以不断优化这些管道。我们在具有不同类型的概念漂移类型的真实和人工数据流中进行实验，以测试拟议系统的性能和适应能力。结果证实了OAML对流行的在线学习算法的实用性，并强调了在存在数据漂移的情况下连续管道重新设计的好处。
Automated Machine Learning (AutoML) has been used successfully in settings where the learning task is assumed to be static. In many real-world scenarios, however, the data distribution will evolve over time, and it is yet to be shown whether AutoML techniques can effectively design online pipelines in dynamic environments. This study aims to automate pipeline design for online learning while continuously adapting to data drift. For this purpose, we design an adaptive Online Automated Machine Learning (OAML) system, searching the complete pipeline configuration space of online learners, including preprocessing algorithms and ensembling techniques. This system combines the inherent adaptation capabilities of online learners with the fast automated pipeline (re)optimization capabilities of AutoML. Focusing on optimization techniques that can adapt to evolving objectives, we evaluate asynchronous genetic programming and asynchronous successive halving to optimize these pipelines continually. We experiment on real and artificial data streams with varying types of concept drift to test the performance and adaptation capabilities of the proposed system. The results confirm the utility of OAML over popular online learning algorithms and underscore the benefits of continuous pipeline redesign in the presence of data drift.
