基于变精度和浓缩布尔矩阵的属性约简

属性约简是粗糙集理论研究的重要内容。传统的基于差别矩阵的属性约简方法只能处理一致决策表,改进的差别矩阵针对决策表中一致和不一致的对象做不同的处理,从而解决了这一问题。浓缩布尔矩阵进一步节省了矩阵的存储空间并提高了矩阵的生成效率,从而可以快速计算得到约简。在此基础上,结合变精度的思想把部分不一致对象合理地加入到一致对象的集合中,从而增加了一致数据的信息量,并通过使用浓缩布尔矩阵有效降低了约简的计算消耗。实验表明,所提方法在运行速度和分类精度方面均表现出了优势。

Attribute Reduction Based on Variable Precision Rough Sets and Concentration Boolean Matrix

Attribute reduction is the most important research topic in rough set theory.The traditional attribute reduction based on discer-nibility matrix can only handle consistent decision tables.Then the concept of improved discernibility matrix was proposed to effectively deal with both consistent and inconsistent decision tables.Further,the condensed Boolean matrix was defined to represent the discernibility matrix in order to save the storage space and improve the efficiency of matrix generation.Based on the previous work,the idea of variable precision was used to select some inconsistent objects in the developing of the discernibility matrix,thus more information can be considered in generating attri-bute reduction.The experimental results show that the proposed method performs advantages in both running speed and classification accuracy.