Multi-granulation rough set model based on granular-ball computing
As one of the important tools for knowledge discovery and data mining,rough set theory based on granular-ball computing has been successfully applied to label prediction and attribute reduction.However,the existing granular-ball rough set models only consider a single granulation,and cannot analyze and process data from a multi-granulation,and there are still many application scenarios that need to be considered from the perspective of multi-granulation.Based on this,this paper proposes a multi-granulation rough set based on granular-ball computing by embedding the idea of granular-ball in the multi-granulation rough set model,and discusses the relevant properties of the model.The model divides the data by setting the purity,which can ef-fectively depict the internal relationship between the data,and thus design a position region generation algo-rithm for multi-granulation granular-ball rough set.Experimental analysis shows the feasibility and effective-ness of this model.
万方数据
维普
