量化粗糙集的单调性属性约简方法  

Approach to Monotonicity Attribute Reduction in Quantitative Rough Set

作　　者：鞠恒荣[1,2] 杨习贝[1,2,3] 戚湧[3] 杨静宇[2] 

机构地区：[1]江苏科技大学计算机科学与工程学院,镇江212003 [2]高维信息智能感知与系统教育部重点实验室,南京210094 [3]南京理工大学经济管理学院,南京210094

出　　处：《计算机科学》2015年第8期36-39,共4页Computer Science

基　　金：国家自然科学基金(61100116;61272419;61305058);江苏省自然科学基金(BK2011492;BK2012700;BK20130471);高维信息智能感知与系统教育部重点实验室(南京理工大学)开放基金(30920130122005);江苏省高校自然科学基金(13KJB520003);江苏省普通高校研究生科研创新计划项目(CXLX13_707)资助

摘　　要：单调性在经典粗糙集属性约简过程中发挥着重要的作用。然而,在一些泛化模型(如量化粗糙集模型)中该性质并不存在。针对该问题,提出了量化粗糙集模型中下近似单调约简的定义,并给出了求得该约简的启发式方法。实验结果表明,相较于下近似保持约简算法,下近似单调约简算法不仅耗时短,而且增加了由正域和边界域表示的确定性,同时降低了由边界域带来的不确定性。It is well-known that the monotonicity plays an important role in attribute reduction of classical rough set. However, sueh property does not always hold in some generalization models, and quantitative rough set is a typical ex- ample. From this point of view, the definition of lower approximate monotonieity attribute reduction was presented in quantitative rough set model, and the heuristic approach was also given to eompute the reduet. The experiment results show that compared with lower approximate preservation reduct, the lower approximate monotonicity can not only save the time consuming, but also increase the certainties which are expressed by positive and negative regions, and decrease the uncertainty coming from boundary region.

关 键 词：单调性 下近似保持 下近似单调 粗糙集 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]