【摘要】 为了能够直接处理不完备的、数值和符号混合的数据,对相容关系和相对邻域关系进行广义化表示,提出一种新的广义邻域关系。在广义邻域关系下,基于信息熵提出一种适用于不完备混合决策系统的条件熵,并证明基于该条件熵的属性重要性包含基于正区域的属性重要性,进而构造基于该条件熵的启发式属性约简算法。采用UCI数据库中6组混合型属性数据集进行仿真实验,通过对比约简后的属性数目、分类精度和运行时间,验证了该约简算法比同类型的其它算法更准确有效。更多还原

【Abstract】 In order to deal with the incomplete,symbol and numeric hybrid data directly,a new kind of generalized neighborhood relationship was constructed by combining with relative neighborhood relationship and tolerance relationship.Under the general neighborhood relationship,the conditional entropy used for incomplete hybrid decision system was defined on the basis of information entropy.It was proved that the attibute significance of the condition entropy contains that of the positive regions in this paper.And then the reduction algorithm based on conditional entropy of incomplete hybrid decision system was constructed.The experiments on six hybrid attribute UCI datasets were made,and the proposed method and the similar methods were compared in aspects of feature gene number,classification accuracy and run-time.The results show that the method of feature gene selection based on the proposed extended rough set model is effective.更多还原