一种基于信息熵加权的属性约简算法

Novel method to attribute reduction based on information entropy weighting

针对现有邻域粗糙集模型中存在属性权重都相同,无法保证关键属性在属性约简时能够被保留的问题,提出了一种基于信息熵加权的属性约简算法。首先,采用了类间熵、类内熵策略,以最大化类间熵最小化类内熵为原则给属性赋予权重;其次,构造了基于加权邻域关系的加权邻域粗糙集模型;最后,基于依赖关系评估属性子集的重要性,从而实现属性约简。在基于UCI数据集上与其他三种属性约简算法进行对比实验,结果表明,该算法能够有效去除冗余,提高分类精度。

Aiming at the problem that attributes in the existing neighborhood rough set model all have the same weight,which cannot ensure that the key attributes can be retained in attribute approximation,this paper proposed an attribute approximation algorithm based on information entropy weighting.Firstly,it adopted interclass entropy and intraclass entropy strategies to assign weights to attributes based on the principle of maximising interclass entropy and minimising intraclass entropy.Secondly,it constructed a weighted neighborhood rough set model based on weighted neighborhood relationships.Finally,it assessed the importance of attribute subsets based on dependency relationships to achieve attribute simplification.Comparison experiments with other three attribute approximation algorithms on UCI-based dataset show that the proposed algorithm can effectively remove redundancy and improve classification accuracy.