基于粗糙集与主成分分析的属性约简的启发式算法研究  

A Heuristic Algorithm for Attribute Reduction Based on Roush Set and PCA

作　　者：王洪伟[1] 吴家春[1] 蒋馥[1] 

机构地区：[1]上海交通大学管理学院智能化管理与计算机仿真研究中心,上海200052

出　　处：《管理工程学报》2004年第3期87-90,共4页Journal of Industrial Engineering and Engineering Management

基　　金：国家自然科学基金资助项目(70271038)

摘　　要：信息系统的属性约简反映了一个决策表的本质信息,为信息系统的数据挖掘奠定的基础。生成一个决策表的所有约简或计算出最佳(定义为属性数目最少)的约简都是NP难问题。目前已有的基于粗糙集的属性约简算法由于计算复杂较高,而不适用大规模数据。本文探讨了一种基于区分矩阵和主成分分析的算法,该算法利用属性在区分矩阵中频率及属性贡献率做为启发规则,寻找最小约简,并在海关通关风险评估系统中得到良好的应用。Attribute reduction of information system is to remove superfluous attributes from information systems while preserving the consistency of classifications the original system provides. Identifying all reductions or the minimal reductions of an information system is already proved to be NP-hard. Therefore, heuristic rules are needed to solve this kind of NP-hard problem with higher efficiency during the reduction finding process. In this paper, we introduce some concepts of rough set relevant to reduction and present an algorithm combining discernibility matrix and principal component analysis(PCA) as heuristic knowledge to find the reduction.

关 键 词：属性约简 粗糙集 区分矩阵 主成分分析 

分 类 号：N94[自然科学总论—系统科学] TP18[自动化与计算机技术—控制理论与控制工程]