Review on novelty detection in the non-stationary environment

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.