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A Survey on Supervised Classification on Data Streams

  • Vincent Lemaire
  • Christophe Salperwyck
  • Alexis Bondu
Chapter
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 205)

Abstract

The last ten years were prolific in the statistical learning and data mining field and it is now easy to find learning algorithms which are fast and automatic. Historically a strong hypothesis was that all examples were available or can be loaded into memory so that learning algorithms can use them straight away. But recently new use cases generating lots of data came up as for example: monitoring of telecommunication network, user modeling in dynamic social network, web mining, etc. The volume of data increases rapidly and it is now necessary to use incremental learning algorithms on data streams. This article presents the main approaches of incremental supervised classification available in the literature. It aims to give basic knowledge to a reader novice in this subject.

Keywords

Support Vector Machine Data Stream Concept Change Incremental Learning Concept Drift 
These 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.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Vincent Lemaire
    • 1
  • Christophe Salperwyck
    • 2
  • Alexis Bondu
    • 2
  1. 1.Orange LabsLannionFrance
  2. 2.EDF R&DClamartFrance

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