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Ensemble Dynamics in Non-stationary Data Stream Classification
[chapter]
2018
Studies in Big Data
Data stream classification is the process of learning supervised models from continuous labelled examples in the form of an infinite stream that, in most cases, can be read only once by the data mining algorithm. One of the most challenging problems in this process is how to learn such models in non-stationary environments, where the data/class distribution evolves over time. This phenomenon is called concept drift. Ensemble learning techniques have been proven effective adapting to concept
doi:10.1007/978-3-319-89803-2_6
fatcat:od6ibg7bnrgzregyd7np6wud4e