ALiPy：Python中的主动学习
ALiPy: Active Learning in Python
摘要：
有监督的机器学习方法通常需要大量的标记样本来进行模型训练。然而，在许多实际应用中，存在大量的未标记数据和有限的标记数据，并且标记的获取是昂贵的。主动学习（AL）通过迭代地选择最有价值的数据来从注释器查询它们的标签，从而降低了标签成本。本文介绍了一个用于主动学习的Python工具箱ALiPy。ALiPy提供了一个基于模块的主动学习框架的实现，它允许用户方便地评估，比较和分析主动学习方法的性能。在工具箱中，学习框架的每个组件都有多个选项可供选择，包括数据处理、主动选择、标签查询、结果可视化等，除了实现20多种最先进的主动学习算法外，ALiPy还支持用户在不同的主动学习设置下轻松配置和实现自己的方法，如AL用于多标签数据，带有噪声注释器的AL，带有不同成本的AL等等。工具箱在Github上有很好的文档和开源，可以通过PyPI轻松安装。
英文摘要：
Supervised machine learning methods usually require a large set of labeled examples for model training. However, in many real applications, there are plentiful unlabeled data but limited labeled data; and the acquisition of labels is costly. Active learning (AL) reduces the labeling cost by iteratively selecting the most valuable data to query their labels from the annotator. This article introduces a Python toobox ALiPy for active learning. ALiPy provides a module based implementation of active learning framework, which allows users to conveniently evaluate, compare and analyze the performance of active learning methods. In the toolbox, multiple options are available for each component of the learning framework, including data process, active selection, label query, results visualization, etc. In addition to the implementations of more than 20 state-of-the-art active learning algorithms, ALiPy also supports users to easily configure and implement their own approaches under different active learning settings, such as AL for multi-label data, AL with noisy annotators, AL with different costs and so on. The toolbox is well-documented and open-source on Github, and can be easily installed through PyPI.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
