随机多属性子空间的ReliefF加权邻域粗糙集与属性约简OA北大核心CSTPCD

Attribute reduction is an important preprocessing method for data dimensionality reduction,but most existing attribute reduction methods do not consider the information of attribute weights in information systems.The ReliefF algorithm is a simple and efficient method for evaluating attribute weights.A ReliefF weighted neighborhood rough set and attribute reduction algorithm based on random multi-attribute subspace is proposed in this paper.Firstly,this method generates multiple sets of attribute set partitions with the same size random subspaces.The local weights of attributes in each set of partitioned random subspaces are calculated using the ReliefF algorithm,and the average of the local weights of attributes obtained from all sets is calculated to obtain the final global weights of each attribute in the information system.Then,based on the results of attribute weights,a new weighted neighborhood rough set model is proposed,and the related theories and properties are proved.Finally,based on this model,an attribute reduction algorithm for information systems is proposed by weighting neighborhood dependency.The experimental results of attribute reduction on public datasets show that the proposed algorithm has better reduction performance than the existing algorithms of the same type.