论文标题
LRA：基于特征选择的局部属性冗余的加速粗糙集框架
LRA: an accelerated rough set framework based on local redundancy of attribute for feature selection
论文作者
论文摘要
在本文中，我们提出并证明了关于决策系统中属性稳定性的定理。根据定理，我们提出了加速粗糙集算法的LRA框架。它是一个通用框架，几乎可以将几乎所有粗糙的设置方法应用于。理论分析可确保高效率。请注意，提高效率不会导致分类准确性的降低。此外，我们为正近似加速框架提供了更简单的证明。
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.
