南京大学学报（自然科学版）2013，Vol.49Issue(2)：210-216,7.
一种有效的基于风险最小化的属性约简算法
An attribute reduction algorithm based on risk minimization
摘要
Abstract
The most important part of the classical rough set theory is upper approximate set and lower approximate set which is defined by accurate set inclusion. However the data at present is not accurate. As we all known,the Pawlak algebra rough set model is too rigid to lack fault-tolerance capability. In order to solve this problem,a bunch of probabilistic rough set models were proposed. Among these models the decision rough set mode! simulated better than others in human intelligence solving problems from semantic perspective.Attribute reduction is one important research field in rough set theory. The decision region,decision rule and the increase or decrease of attributes are not following monotonicity in the models of probability decision rough set,the positive region,negative region and border region of decision table maybe change differently before and after attribute reduction,therefore it's a important problem to evaluate attribute reduction is appropriate according to regions change. Therefore the definition of attribute reduction based on risk minimization under decision rough set model was proposed in order to solve this problem.The definition of attribute reduction based on rough set theory mostly requires that positive or nonnegative region are the same as before. Decision region, decision rule and the increase or decrease of attributes are not following monotonicity in the model of probability decision rough set. Therefore, it is very meaningful to minimize the decision risk according to attributes sets after reduction for decision makers. This paper studies the attribute reduction based on the minimum risk decision which has nothing to do with every region. Considering different attributes has different abilities to decide and make classification to decision table, an attribute significance concept based on decision rough set model was proposed, then we propose an effective decision-making risk minimization heuristic attribute reduction algorithm based on attribute significance. Examples analysis and experiment results comparison show the new method is effective.关键词
属性约简/风险最小化/属性重要性/决策租糙集Key words
attribute reduction/minimum decision cost/significance of attributes/decision-theoretic rough set引用本文复制引用
于洪,姚园,赵军..一种有效的基于风险最小化的属性约简算法[J].南京大学学报（自然科学版）,2013,49(2):210-216,7.基金项目
国家自然科学基金(61073146,61272060),重庆市自然科学基金(cstc2011jjA40045) （61073146,61272060）
