LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
Chongqing University of Telecommunications and Posts（重庆邮电大学）；Tianjin University（天津大学）；University of California Riverside（加州大学河滨分校）
英文摘要：
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.
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