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On Learnability of Decision Tables

  • Wojciech Ziarko
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3066)

Abstract

The article is exploring the learnabilty issues of decision tables acquired from data within the frameworks of rough set and of variable precision rough set models. Measures of learning problem complexity and of learned table domain coverage are proposed. Several methods for enhancing the learnabilty of decision tables are discussed, including a new technique based on value reducts.

Keywords

Decision Table Approximation Space Decision Attribute Information Table Decision Class 
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-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Wojciech Ziarko
    • 1
  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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