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Ensemble learning for data stream analysis: A survey
2017
Information Fusion
In many applications of information systems learning algorithms have to act in dynamic environments where data are collected in the form of transient data streams. Compared to static data mining, processing streams imposes new computational requirements for algorithms to incrementally process incoming examples while using limited memory and time. Furthermore, due to the non-stationary characteristics of streaming data, prediction models are often also required to adapt to concept drifts. Out of
doi:10.1016/j.inffus.2017.02.004
fatcat:rfc735znxjcwdebcbjxbyx7xki