This Title All WIREs
How to cite this WIREs title:
WIREs Data Mining Knowl Discov
Impact Factor: 7.250

An overview of unsupervised drift detection methods

Full article on Wiley Online Library:   HTML PDF

Can't access this content? Tell your librarian.

Abstract Practical applications involving big data, such as weather monitoring, identification of customer preferences, Internet log analysis, and sensors warnings require challenging data analysis, since these are examples of problems whose data are generated in streams and usually demand real‐time analytics. Patterns in such data stream problems may change quickly. Consequently, machine learning models that operate in this context must be updated over time. This phenomenon is called concept drift in machine learning and data mining literature. Several different directions have been pursued to learn from data stream and to deal with concept drift. However, most drift detection methods consider that an instance's class label is available right after its prediction, since these methods work by monitoring the prediction results of a base classifier or an ensemble of classifiers. Nevertheless, this constraint is unrealistic in several practical problems. To cope with this constraint, some works are focused on proposing efficient unsupervised or semi‐supervised concept drift detectors. While interesting and recent overview papers dedicated to supervised drift detectors have been published, the scenario is not the same in terms of unsupervised methods. Therefore, this work presents a comprehensive overview of approaches that tackle concept drift in classification problems in an unsupervised manner. Additional contribution includes a proposed taxonomy of state‐of‐the‐art approaches for concept drift detection based on unsupervised strategies. This article is categorized under: Technologies > Classification Technologies > Machine Learning
The increasing number of publications as surveys, overviews, and reviews on concept drift detection published in the last decade
[ Normal View | Magnified View ]
A general framework of unsupervised online‐based drift detection methods
[ Normal View | Magnified View ]
Instance selection strategies used in the partial‐batch detection methods surveyed
[ Normal View | Magnified View ]
A general framework of unsupervised batch‐based drift detection methods
[ Normal View | Magnified View ]
Proposed taxonomy of unsupervised concept drift detection methods
[ Normal View | Magnified View ]

Browse by Topic

Technologies > Machine Learning
Technologies > Classification

Access to this WIREs title is by subscription only.

Recommend to Your
Librarian Now!

The latest WIREs articles in your inbox

Sign Up for Article Alerts