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Classification

  • Living reference work entry
  • First Online:
Encyclopedia of Database Systems

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Synonyms

Classification learning; Concept learning; Learning with a teacher; Statistical decision techniques; Supervised learning

Definition

In Classification learning, an algorithm is presented with a set of classified examples or “instances” from which it is expected to infer a way of classifying unseen instances into one of several “classes”. Instances have a set of features or “attributes” whose values define that particular instance. Numeric prediction, or “regression,” is a variant of classification learning in which the class attribute is numeric rather than categorical. Classification learning is sometimes called supervised because the method operates under supervision by being provided with the actual outcome for each of the training instances. This contrasts with clustering where the classes are not given, and with association learning which seeks any association – not just one that predicts the class.

Historical Background

Classification learning grew out of two strands of...

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Recommended Reading

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Author information

Authors and Affiliations

  1. University of Waikato, Hamilton, New Zealand

    Ian H. Witten

Authors
  1. Ian H. Witten
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Corresponding author

Correspondence to Ian H. Witten .

Editor information

Editors and Affiliations

  1. Georgia Institute of Technology College of Computing, Atlanta, Georgia, USA

    Ling Liu

  2. University of Waterloo School of Computer Science, Waterloo, Ontario, Canada

    M. Tamer Özsu

Section Editor information

  1. School of Elec. Eng. and Computer Science, Seoul National Univ., Kwanak P.O. Box 34, 151-742, Seoul, Republic of Korea

    Kyuseok Shim

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© 2016 Springer Science+Business Media New York

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Witten, I.H. (2016). Classification. In: Liu, L., Özsu, M. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4899-7993-3_552-2

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  • DOI: https://doi.org/10.1007/978-1-4899-7993-3_552-2

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  • Publisher Name: Springer, New York, NY

  • Online ISBN: 978-1-4899-7993-3

  • eBook Packages: Springer Reference Computer SciencesReference Module Computer Science and Engineering

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