![]() In this scenario, it is not possible to use traditional approaches. In the more challenging case, there are applications with extreme latency, where in after the classification of the examples, heir actual class labels are never available to the algorithm. Given the high label costs, it is more reasonable to consider that this delay could vary for the most portion of the data. In the update phase, most of the approaches assume that after the classification of each example from the stream, their actual class label is available without any t ime delay (zero latency). Thus, the classifiers that deal with data streams require constants updates in their classification models to maintain a stable accuracy over time. Due to non-stationarity of the environment that generates the data, the features that describe the concepts of the classes can change over time. Among possible tasks that can be performed with these data, classification is one of the most prominent. Many applications are able to generate data continuously over t ime in an ordered and uninterrupted way in a dynamic environment, called data streams. ![]()
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