A streaming ensemble algorithm (SEA) for large-scale classification
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Learning Changing Concepts by Exploiting the Structure of Change
Machine Learning
Decision trees for mining data streams
Intelligent Data Analysis
Using diversity to handle concept drift in on-line learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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Concept drift in data is usually considered only as abrupt or gradual thus referring to the speed of change. Such simple distinguishing by speed is sufficient for most of the problems, but there might be situations for which a finer representation would be of use. This paper studies further the phenomenon of concept drift and introduces a simple measure which is relevant to the speed and amount of change between different concepts.