Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems

نویسندگان

  • Vu Van Dinh
  • Nguyen Long Giang
  • Vu Duc Thi
  • Marzena Kryszkiewicz
چکیده

A rough set approach for attribute reduction is an important research subject in data mining and machine learning. However, most attribute reduction methods are performed on a complete decision system table. In this paper, we propose methods for attribute reduction in static incomplete decision systems and dynamic incomplete decision systems with dynamically-increasing and decreasing conditional attributes. Our methods use generalized discernibility matrix and function in tolerance-based rough sets.

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تاریخ انتشار 2014