基于邻域粗糙集的符号与数值属性快速约简算法

Efficient Symbolic and Numerical Attribute Reduction with Neighborhood Rough Sets

粗糙集理论被广泛应用于属性约简,算法复杂性是制约约简应用于大样本知识发现的主要问题,尤其是邻域模型下的约简问题.本文分析邻域粗糙集模型的数学性质,利用正域与属性集的单调关系,构造基于属性依赖度和前向搜索策略的快速算法.该算法降低样本比较次数,提高计算效率.实验分析表明该算法的有效性.

Rough set theory is widely used in attribute reduction. Computational complexity is one of the factors to limit applicability in reduction techniques, especially in Pthe neighborhood rough set based reduction. In this paper, some mathematical properties of neighborhood rough set model are analyzed. An efficient method is proposed for forward attribute selection strategy based on dependency by using the property that positive region monotonously increases with the amount of attributes. By this algorithm, the comparison times of the samples in computing positive region and neighborhood are reduced, and thus the computational efficiency is improved. The experimental results show that the proposed method is effective.