Incremental Learning From Unbalanced Data with Concept Class, Concept Drift and Missing Features : A Review

@inproceedings{Kulkarni2014IncrementalLF,
  title={Incremental Learning From Unbalanced Data with Concept Class, Concept Drift and Missing Features : A Review},
  author={Pallavi P. Kulkarni and Roshani Ade},
  year={2014}
}
Recently, stream data mining applications has drawn vital attention from several research communities. Stream data is continuous form of data which is distinguished by its online nature. Traditionally, machine learning area has been developing learning algorithms that have certain assumptions on underlying distribution of data such as data should have predetermined distribution. Such constraints on the problem domain lead the way for development of smart learning algorithms performance is… CONTINUE READING
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