LRA: an accelerated rough set framework based on local redundancy of
  attribute for feature selection
  In this paper, we propose and prove the theorem regarding the stability of
attributes in a decision system. Based on the theorem, we propose the LRA
framework for accelerating rough set algorithms. It is a general-purpose
framework which can be applied to almost all rough set methods significantly .
Theoretical analysis guarantees high efficiency. Note that the enhancement of
efficiency will not lead to any decrease of the classification accuracy.
Besides, we provide a simpler prove for the positive approximation acceleration
framework.
