Novelty detection and concept drift detection are essential for the plethora of machine learning applications. The statistical properties of application generated data change over time in the streaming environment, known as concept drift. These changes develop a profound influence on the learning model’s performance. Along with concept drift, the new class emergence (i.e., novel class/novelty detection) is also challenging in the non-stationary distribution of data. Novel class detection finds whether the identifying data points of a data stream are unknown or unusual. The paper presents a survey focusing on the challenges encountered while dealing with real-time data. In addition to this, the chronological discussion on the various existing novelty detectors with their advantages, limitations, critical points, the different research prospect, and future directions are also incorporated in the paper.

新颖性检测和概念漂移检测对于大量机器学习应用至关重要。应用程序生成的数据的统计属性在流环境中随时间变化，称为概念漂移。这些变化对学习模型的性能产生了深远的影响。除了概念漂移之外，新类别的出现（即新颖类别/新颖性检测）在数据的非平稳分布中也面临着挑战。新类别检测可发现数据流的识别数据点是否未知或异常。本文提出了一项调查，重点关注处理实时数据时遇到的挑战。除此之外，本文还按时间顺序讨论了现有的各种新颖探测器及其优点、局限性、临界点、不同的研究前景和未来方向。