Uncertainty Measure and Attribute Reduction in Inconsistent Neighborhood Rough Set
YAO Sheng1，2，WANG Jie1，2，XU Feng1，2，CHEN Ju1，2
1（Key Lab of IC&SP of Ministry of Education（Anhui University），Hefei 230601，China）
2（College of Computer Science and Technology，Anhui University，Hefei 230601，China）
Abstract：Uncertainty measure and attribute reduction are the important research contents in neighborhood rough set model.In this paper，it is difficult to apply the rough set uncertainty measure method to the neighborhood rough set.Considering that the relationship between the conditional attributes is rarely considered in the existing attribute reduction algorithm，the reduction result and classification accuracy are also affected.we first analyze the correlation properties of the inconsistent neighborhood rough sets，and then propose the uncertainty measure method of neighborhood entropy to evaluate the quality of the reduced attributes.The related property theorems are proved and then the rank correlation The concept of coefficient is used to eliminate the redundant attributes by calculating the correlation coefficient between conditional attributes，and the attribute reduction algorithm of inconsistent neighborhood rough set based on correlation coefficient（RNRS） is constructed.Finally，the UCI data sets are compared with the existing algorithms.The experimental results show that the algorithm can obtain fewer attribute characteristics and high classification accuracy.
2018, Vol. 39 
