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Rule Induction from Rough Approximations

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Springer Handbook of Computational Intelligence

Part of the book series: Springer Handbooks ((SHB))

Abstract

Rule induction is an important technique in data mining or machine learning. Knowledge is frequently expressed by rules in many areas of artificial intelligence (GlossaryTerm

AI

), including rule-based expert systems. In this chapter we discuss only supervised learning in which all cases of the input data set are pre-classified by an expert.

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Abbreviations

AI:

artificial intelligence

LEM:

learning from examples module

LERS:

learning from examples using rough sets

MLEM2:

modified LEM2 algorithm

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Author information

Authors and Affiliations

  1. Dep. Electrical Engineering and Computer Science, University of Kansas, 3014 Eaton Hall, 1520 W. 15th St., KS 66045-7621, Lawrence, USA

    Jerzy W. Grzymala-Busse

Authors
  1. Jerzy W. Grzymala-Busse
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Correspondence to Jerzy W. Grzymala-Busse .

Editor information

Editors and Affiliations

  1. Systems Research Inst., Polish Academy of Sciences, ul. Newelska 6, 01-447, Warsaw, Poland

    Janusz Kacprzyk

  2. Dep. Electrical and Computer Engineering, University of Alberta, 116 Street 9107, T6J 2V4, Edmonton, Alberta, Canada

    Witold Pedrycz

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Grzymala-Busse, J.W. (2015). Rule Induction from Rough Approximations. In: Kacprzyk, J., Pedrycz, W. (eds) Springer Handbook of Computational Intelligence. Springer Handbooks. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-43505-2_23

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  • DOI: https://doi.org/10.1007/978-3-662-43505-2_23

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-43504-5

  • Online ISBN: 978-3-662-43505-2

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