J4 ›› 2012, Vol. 47 ›› Issue (1): 72-76.

• Articles • Previous Articles     Next Articles

Approaches for attribute core and attribute reduction based on an  improved extended positive region

FENG Lin1,2, LUO Feng3, FANG Dan3, YUAN Yong-le1   

  1. 1. Sichuan Key Laboratory of Visualization Computing and Virtual Reality, Sichuan Normal University, Chengdu 610068,
    Sichuan, China; 2. College of Computer Science, Sichuan Normal University, Chengdu 610101, Sichuan, China;
    3. College of Technology, Sichuan Normal University, Chengdu 610101, Sichuan, China
  • Received:2011-06-17 Online:2012-01-20 Published:2012-06-29

Abstract:

The disadvantage of the existing approaches for extending the positive region from the inconsistency decision table is  analyzed. Based on the self-learning model under uncertain condition, a new approach for extending the positive region is established. Finally, algorithms for calculating cognitive attribute core and cognitive attribute reduction are developed. Simulation results illustrate the efficiency of these algorithms.

Key words:  rough set; decision-theoretic rough set; attribute reduction; core attribute

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