基于联合属性重要度的决策风险最小化属性约简  

Attribute Reduction Based on Cost Minimization and Significance of Joint Attributes

作　　者：徐菲菲[1] 毕忠勤[1] 雷景生[1] 

机构地区：[1]上海电力学院计算机科学与技术学院,上海200090

出　　处：《计算机科学》2016年第S1期40-43,共4页Computer Science

基　　金：国家自然科学基金(61272437;61305094);上海市教育发展基金会和上海市教育委员会"晨光计划"(13CG58);上海市自然科学基金(13ZR1417500)资助

摘　　要：经典粗糙集属性约简基本都是保持正域、负域和边界域不变,而决策粗糙集对属性的增减过程不具备单调性,因此不可能同时保持3个区域均不变。在决策粗糙集模型中,作出决策更应该考虑风险最小化原则,因此提出一种改进的风险最小化属性约简方法,在属性的选取过程中同时考虑所选取的属性子集对决策的划分能力,即联合属性重要度以及风险最小化。实验证明所提方法是有效的。The classical rough set attribute reduction is mainly based on maintaining positive region,boundary region and negative region unchanged.In the decision rough set model,the reduction procedure for adding or deleting an attribute is no longer monotonous,so that three regions can not keep all unchanged.In decision theoretic rough set model,decision making should take consideration of minimizing the cost.Therefore,this paper put forward a method for attribute reduction based on minimizing the cost,while considering the classification ability of selected attribute subset to the decision-making,which is named as the significance of joint attributes.Experiments show that our method is effective.

关 键 词：属性约简 风险最小化 联合属性重要度 决策粗糙集 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]