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- W836115342 abstract "AbstractThe problem of data stream mining is widely studied in the literature. Especially difficult to solve is the problem of mining data with occurring concept drift. The most commonly used algorithms are those based on decision trees. In this article we investigate the performance of a few algorithms of constructing decision trees for data stream classification, not explicitly designed to deal with changing distribution of data. We show how to adapt these methods to deal with concept drift and we compare the obtained results.KeywordsDecision TreeData StreamData ElementGini IndexConcept DriftThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
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- W836115342 date "2013-01-01" @default.
- W836115342 modified "2023-09-23" @default.
- W836115342 title "Adaptation of Decision Trees for Handling Concept Drift" @default.
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- W836115342 doi "https://doi.org/10.1007/978-3-642-38658-9_41" @default.
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