多粒度粒球粗糙集模型

Multi-granulation rough set model based on granular-ball computing

基于粒球计算的粗糙集理论作为知识发现和数据挖掘的重要工具之一,已成功地应用于标记预测、属性约简等。而现有的粒球粗糙集模型仅仅是从单粒度出发,无法从多粒度角度对数据进行分析和处理,实际生活中仍有很多应用场景需从多粒度角度进行思考。将粒球计算思想结合到多粒度粗糙集模型,提出了多粒度粒球粗糙集模型,并讨论了该模型的相关性质。该模型通过纯度的设定对数据进行粒球划分,能够有效地刻画数据之间的内在联系,以此设计多粒度粒球粗糙集的正域生成算法。实验分析表明该模型的可行性和有效性。

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 effectively depict the internal relationship between the data,and thus design a position region generation algorithm for multi-granulation granular-ball rough set.Experimental analysis shows the feasibility and effectiveness of this model.