粗集样本冗余性处理的改进研究

The improved redundancy handling of rough set processing of samples

针对粗糙集属性约简过程中冗余属性所导致的时间空间复杂度增大的问题,提出了一种改进的精简信息系统的冗余性处理算法。经过Keil uVision3仿真结果验证,当被删除的冗余属性值足够大到与粗集样本接近时,新算法大大减小了时间和空间复杂度,表明了新算法的有效性和高效性。

For rough set attribute reduction process, the Redundant attributes caused by the growing problem of the time and space complexity. Proposed a simplified information system to improve redundancy processing algorithms.Through the Keil uVision3 simulation results demonstrating,When removed redundant attribute values is large enough to close to the samples with the rough set. The new algorithm significantly reduces the time and space complexity, the new algorithm shows the effectiveness and efficiency.