基于局部条件区分能力的高效属性约简算法

基于区分矩阵的传统属性约简方法具有直观易理解的优点，但时间和空间复杂度都很高，当数据规模较大或条件属性较多时，会无法快速得到约简结果。为解决该问题，在区分关系的基础上构造了条件区分能力来进行属性选择，提出一种基于条件区分能力的属性约简算法。而为了进一步加快属性重要性的计算、提高约简效率，依据大数定律中频率的稳定性，通过采样的方式将条件区分能力扩展为局部条件区分能力，提出基于局部条件区分能力的属性约简算法。理论证明了条件区分能力在属性的选择上比正区域更严格，并将该算法与基于区分度的高效前向属性约简算法（FAR-DV）、基于k近邻属性重要度和相关系数的属性约简算法（K2NCRS）及基于正区域排序升序决策表的快速正区域约简算法（FPRA）进行了对比。实验结果显示，该算法在属性选择顺序、约简率和分类精度上与FAR-DV基本一致，在约简效率上比上述三种算法提高了10倍以上；且随着数据规模的增大或条件属性的增多，在约简效率上的提升越明显。可以看出，所提算法具有更低的时空复杂度，适用于海量数据属性约简。

Efficient attribute reduction algorithm based on local conditional discernibility

The traditional attribute reduction method based on discernibility matrix is intuitive and easy to understand. However， its time and space complexities are high， so when dealing with large scale data or many conditional attributes， it will not be able to get the reduction result quickly. In order to solve the problem， the conditional discernibility was constructed based on the discernibility relation for attribute selection， and an attribute reduction algorithm based on conditional discernibility was proposed. In order to further accelerate the calculation of attribute importance and improve the efficiency of attribute reduction， according to the stability of frequency in the law of large numbers， the conditional discernibility was extended to local conditional discernibility by sampling， and an attribute reduction algorithm based on local conditional discernibility was proposed. It was theoretically proved that the conditional discernibility was stricter than the positive region in attribute selection. And this algorithm was compared with efficient Forward Attribute Reduction algorithm from Discernibility View （FAR-DV）， attribute reduction algorithm based on k-Nearest Neighbor attribute importance and Correlation Coefficient （K2NCRS） and Fast Positive Region reduction Algorithm based on positive region sort ascending decision table （FPRA）. Experimental results show that the proposed algorithm is similar to FAR-DV in attribute selection order， reduction rate and classification accuracy. Compared with the above three algorithms， the proposed algorithm has the reduction efficiency improved more than ten times. With the increase of data scale or conditional attributes， the reduction efficiency improvement of this algorithm is higher. It can be seen that the proposed algorithm has lower time and space complexities and is suitable for the attribute reduction of massive data.