基于依赖度的区间集决策信息表属性约简  

Attribute reduction based on dependency degree for interval-set decision information tables

作　　者：唐鹏飞 张贤勇 莫智文[1,2] Tang Pengfei;Zhang Xianyong;Mo Zhiwen(School of Mathematical Sciences,Sichuan Normal University,Chengdu 610066,China;Institute of Intelligent&Quantum Information,Sichuan Normal University,Chengdu 610066,China)

机构地区：[1]四川师范大学数学科学学院,成都610066 [2]四川师范大学智能信息与量子信息研究所,成都610066

出　　处：《计算机应用研究》2021年第11期3300-3303,3309,共5页Application Research of Computers

基　　金：国家自然科学基金资助项目(61673258,11671284);四川省科技基金资助项目(2021YJ0085,2019YJ0529,2020YFG0290)。

摘　　要：区间集决策信息表拓展了经典决策信息表,但其属性约简研究较少。针对区间集决策信息表存在的问题,采用模型正域及相关依赖度提出属性约简及其启发式约简算法。在区间集粗糙集模型中,定义关于决策分类的正域与依赖度,证明粒化单调性等性质。提出基于依赖度的属性约简,设计启发式约简算法。实例分析与数据实验表明,设计的基于依赖度的启发式约简算法是有效的,所得结果有利于依赖学习与特征优化。Interval-set decision information tables extend classical decision information tables.However,few studies have focused on its heuristic reduction.To solve the problem in interval-set decision information tables,this paper proposed attribute reduction and its heuristic reduction algorithm by adopting the model positive region and corresponding dependency degree.In the interval-set rough set model,it defined the positive region and dependency degree for decision classification,and proved the granulation monotonicity.It established attribute reduction based on the dependency degree and designed a heuristic reduction algorithm.The instance analysis and the experimental results show that the proposed attribute reduction algorithm is effective.The obtained results are useful for dependency learning and feature optimization.

关 键 词：粗糙集 区间集粗糙集 区间集决策信息表 依赖度 属性约简 启发式约简算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]