An attribute reduction algorithm of weighting neighborhood rough sets with Critic method
Compared with classical rough sets,neighborhood rough sets can process non-discrete and high-dimensional data,and get simplified data without reducing the ability of data processing.An attribute reduction approach of weighting neighborhood rough sets using the Critic method is proposed,aiming at the problem that every attribute in neighborhood rough sets has the same weight and every attribute has varied influence on decision making.Firstly,the Critic method is used to weigh the conditional attributes,the weighted distance function is introduced to calculate the neighborhood relationship,and then the weighted neighborhood relationship is obtained.Secondly,the weighted neighborhood rough sets are constructed,the attribute dependency and importance are used to evaluate the importance of the subset,the isometric search is used to find the best threshold,attribute reduction is carried out,and the optimal attribute subset is found.Finally,the experimental verification is carried out with 10 data sets in the UCI database,and the performance of the attribute reduction algorithm is compared with that of traditional neighborhood rough sets.The outcomes of the experiment demonstrate that the algorithm is able to guarantee the classification accuracy of the reduced data in addition to obtaining the minimum attribute reduction set.It has effectiveness and practical application value.
万方数据
维普
