Classifying Attributes with Game-Theoretic Rough Sets

  • Nouman Azam
  • JingTao Yao
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 15)


Attributes may be categorized as core, reduct or non-reduct attributes when rough set theory is utilized for classification. These attribute types play different roles in feature selection algorithms. We introduce a game-theoretic rough set based method that formulates the classification of an attribute as a decision problem within a game. In particular, multiple measures representing importance levels for an attribute are incorporated into a unified framework to obtain an effective attribute classification mechanism. Demonstrative example suggests that the method may be efficient in classifying different types of attributes.


Payoff Function Feature Selection Algorithm Core Attribute Attribute Importance Discernibility Matrix 
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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© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of ReginaReginaCanada

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