可变多粒球粗糙集模型

Variable multi-granularity ball rough set model

构建与乐观多粒球粗糙集模型对应的悲观多粒球粗糙集模型,并设计一种介于这2种模型之间的可变多粒球粗糙集模型,以适用于不同决策问题.讨论该模型的相关性质和提出相关的不确定性度量,并设计3种不同的多粒球粗糙集模型正域生成算法.该算法通过纯度的设置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.最后,通过对八组UCI数据集的实验分析验证该模型的可行性和有效性.

A pessimistic multi-granularity ball rough set model is developed as the counterpart to the optimistic model,and a variable multi-granularity ball rough set model is designed to address different decision-making problems.The relevant properties of this model are explored,and uncertainty measures associated with it are introduced.Three distinct positive region generation algorithms for multi-granular ball rough set models are proposed.These algorithms partition the data into granular balls by adjusting the purity parameter,effectively capturing the inherent relationships within the data.Finally,experimental evaluations on eight UCI datasets confirm the feasibility and effectiveness of the proposed model.