Concept Drift Detection in Data Stream Mining : A literature review

Abstract

In recent years, the availability of time series streaming information has been growing enormously. Learning from real-time data has been receiving increasingly more attention since the last decade. Online learning encounters the change in the distribution of data while extracting considerable information from data streams. Hidden data contexts, which are not known to the learning algorithms, are known as concept drift. Classifier classifies incoming instances using past training instances of the data stream. The accuracy of the classifier deteriorates because of the concept drift. The traditional classifiers are not expected to learn the patterns in a non-stationary distribution of data. For any real-time use, the classifier needs to detect the concept drift and adapts over time. In the real-time scenario, we have to deal with semi-supervised and unsupervised data, which provide no or fewer labeled data. The motivation behind this paper is to introduce a survey identified with a broad categorization of concept drift detectors with their key points, limitations, and advantages. Eventually, the article suggests research trends, research challenges, and future work. The adaptive mechanisms are also incorporated in this survey.

Keywords

• Concept drift
• Concept evolution
• Adaptation mechanism
• Data stream mining