A review of tracking concept drift detection in machine learning
Nail Adeeb Ali Abdu, Khaled Omer Basulaim · 2023
The availability of time series streaming data has increased dramatically in recent years. Since the last decade, there has been a growing interest in learning from real-time data. While extracting significant information from data streams, online learning faces changes in data distribution. Concept drift refers to hidden data contexts that learning systems are unaware of it. Using previous training instances from the data stream, the classifier categorizes new instances. Hence in this regard, it is obvious to have considerable methods to discover the similarity or diversity status of the present streaming data that compared to the buffered data. This issue is defined and explored in contemporary literature as concept drift. This paper was initiated to identify the constraints of the contemporary methods devised to handle the concept drift in data streams in relation to supervised learning, we also review and categories the concept drift detectors with their key points. Eventually, the article presented observations can provide decision boundary detection at improved levels and discussing open research challenges and possible new research direction.