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LERS-A System for Learning from Examples Based on Rough Sets

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Intelligent Decision Support

Part of the book series: Theory and Decision Library ((TDLD,volume 11))

Abstract

The paper presents the system LERS for rule induction. The system handles inconsistencies in the input data due to its usage of rough set theory principle. Rough set theory is especially well suited to deal with inconsistencies. In this approach, inconsistencies are not corrected. Instead, system LERS computes lower and upper approximations of each concept. Then it induces certain rules and possible rules. The user has the choice to use the machine learning approach or the knowledge acquisition approach. In the first case, the system induces a single minimal discriminant description for each concept. In the second case, the system induces all rules, each in the minimal form, that can be induced from the input data. In both cases, the user has a choice between the local or global approach.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University of Kansas, Lawrence, KS, 66045, USA

    Jerzy W. Grzymala-Busse

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  1. Jerzy W. Grzymala-Busse
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Editors and Affiliations

  1. Institute of Computing Science, Technical University of Poznań, Poland

    Roman Słowiński

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© 1992 Springer Science+Business Media Dordrecht

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Grzymala-Busse, J.W. (1992). LERS-A System for Learning from Examples Based on Rough Sets. In: Słowiński, R. (eds) Intelligent Decision Support. Theory and Decision Library, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7975-9_1

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  • DOI: https://doi.org/10.1007/978-94-015-7975-9_1

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-90-481-4194-4

  • Online ISBN: 978-94-015-7975-9

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