Stream Classification [chapter]

Jerzy Stefanowski, Dariusz Brzezinski
<span title="">2017</span> <i title="Springer US"> Encyclopedia of Machine Learning and Data Mining </i> &nbsp;
An entry to appear in the Encyclopedia of Machine Learning (Springer) Definition Stream classification is a variant of incremental learning of classifiers that has to satisfy requirements specific for massive streams of data: restrictive processing time, limited memory, and one scan of incoming examples. Additionally, stream classifiers often have to be adaptive, as they usually act in dynamic, non-stationary environments where data and target concepts can change over time. To fulfill these
more &raquo; ... irements new solutions include dedicated data management and forgetting mechanisms, concept drift detectors that monitor the underlying changes in the stream, effective online single classifiers, and adaptive ensembles that continuously react to changes in the streams. Motivation and Background In many data intensive applications, like sensor networks, traffic control, market analysis, Web user tracking, and social media, massive volumes of data are continuously generated in the form of data streams. A data stream is a potentially unbounded, ordered sequence of data items, which arrive continuously at high-speeds. These data elements can be simple attributevalues pairs like relational database tuples or more complex structures such as graphs. The main characteristics of streams include: • continuous flow (elements arrive one after another), • huge data volumes (possibly of an infinite length), • rapid arrival rate (relatively high with respect to the processing power of the system),
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