Non-stationary data can be characterized as data having a distribution that changes over time. It is well-known that most successful machine learning algorithms are based on stationary data i.e., data that are assumed to have a fixed distribution (although unknown, in most cases). Non-stationary classification problems require the induced classifiers to be flexible enough to learn or adapt themselves...
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