介粒度空间中的最优粒度选择和属性约简  

Optimal Granularity Selection and Attribute Reduction in Meso-granularity Space

作　　者：李腾 李德玉[1,2] 翟岩慧[1,2] 张少霞 LI Teng;LI Deyu;ZHAI Yanhui;ZHANG Shaoxia(School of Computer and Information Technology,Shanxi University,Taiyuan 030006,China;Key Laboratory of Computational Intelligence and Chinese Information Processing(Shanxi University),Ministry of Education,Taiyuan 030006,China;College of Information,Shanxi University of Finance and Economics,Taiyuan 030006,China)

机构地区：[1]山西大学计算机与信息技术学院,太原030006 [2]计算智能与中文信息处理教育部重点实验室(山西大学),太原030006 [3]山西财经大学信息学院,太原030006

出　　处：《计算机科学》2023年第10期71-79,共9页Computer Science

基　　金：国家自然科学基金(62072294,61972238);山西省基础研究计划资助项目(202103021223303)。

摘　　要：以往的形式概念分析采用介粒度形式背景,满足对数据跨层粒化的需求,但其既没有将寻找最优粒度和属性约简有效结合起来,又没有在多粒度的背景下高效地解决组合爆炸问题。为此,基于介粒度中粒度选择和属性约简的联系,提出了一种新的最优粒度选择方式——最优粒度约简,以同步进行粒度选择和属性约简。鉴于寻找最优粒度约简存在组合爆炸的问题,设计了逐步搜索方法,通过已搜索的信息更新粒度空间,去除大量非最优粒度约简,显著提高了搜索效率。实验结果表明了所提方法的有效性和优势。The conventional formal concept analysis adopts a meso-granularity formal context to meet the requirements of cross-layer granulation of data.However,it does not effectively combine the search for optimal granularity with attribute reduction,nor does it efficiently solve the problem of combination explosion in a multi-granular context.Therefore,based on the connection between granularity selection and attribute reduction in the meso-granularity,a new optimal granularity selection method(i.e.,optimal granularity reduction)is proposed to synchronize the selection of the optimal granularity and attribute reduction.In view of the combination explosion in searching for optimal granularity reduction,a stepwise search method is designed to update the gra-nularity space with searched information,eliminating a large number of non-optimal granularity reduction and significantly improving search efficiency.Experimental results demonstrate the effectiveness and superiority of this method.

关 键 词：形式概念分析 多粒度决策形式背景 最优粒度 属性约简 粒计算 

分 类 号：TP182[自动化与计算机技术—控制理论与控制工程]