Developing effective and efficient data stream classifiers is challenging for the machine learning community because of the dynamic nature of data streams. As a result, many data stream learning algorithms have been proposed during the past decades and achieve great success in various fields. This paper aims to explore a specific type of challenge in learning evolving data streams, called concept evolution (emergence of novel classes). Concept evolution indicates that the underlying patterns evolve over time, and new patterns (classes) may emerge at any time in streaming data. Therefore, data stream classifiers with emerging class detection have received increasing attention in recent years due to the practical values in many real-world applications. In this article, we provide a comprehensive overview of the existing works in this line of research. We discuss and analyze various aspects of the proposed algorithms for data stream classification with concept evolution detection and adaptation. Additionally, we discuss the potential application areas in which these techniques can be used. We also provide a detailed overview of evaluation measures and datasets used in these studies. Finally, we describe the current research challenges and future directions for data stream classification with novel class detection.

由于数据流的动态特性，开发有效且高效的数据流分类器对机器学习社区来说具有挑战性。因此，在过去的几十年里，人们提出了许多数据流学习算法，并在各个领域取得了巨大的成功。本文旨在探索学习进化数据流中的一种特定类型的挑战，称为概念进化（新类的出现）。概念演化表明底层模式随时间演化，流数据中随时可能出现新的模式（类）。因此，由于在许多实际应用中的实用价值，具有新兴类别检测的数据流分类器近年来受到越来越多的关注。在本文中，我们对这一研究领域的现有工作进行了全面概述。我们讨论和分析了所提出的数据流分类算法的各个方面，包括概念演化检测和适应。此外，我们还讨论了可以使用这些技术的潜在应用领域。我们还提供了这些研究中使用的评估措施和数据集的详细概述。最后，我们描述了使用新型类检测进行数据流分类的当前研究挑战和未来方向。我们还提供了这些研究中使用的评估措施和数据集的详细概述。最后，我们描述了使用新型类检测进行数据流分类的当前研究挑战和未来方向。我们还提供了这些研究中使用的评估措施和数据集的详细概述。最后，我们描述了使用新型类检测进行数据流分类的当前研究挑战和未来方向。