基于邻域粗糙互信息熵的非单调性属性约简

属性约简是粗糙集理论一项重要的应用,目前已广泛运用于机器学习和数据挖掘等领域,邻域粗糙集是粗糙集理论中处理连续型数据的一种重要方法.针对目前邻域粗糙集模型中属性约简存在的缺陷,构造一种基于邻域粗糙集的邻域粗糙熵模型,并基于此给出邻域粗糙联合熵、邻域粗糙条件熵和邻域粗糙互信息熵等概念.邻域粗糙互信息熵是评估属性集相关性的一种重要的方法,具有非单调性变化的特性,对此,提出一种基于邻域粗糙互信息熵的非单调性属性约简算法.实验分析表明,所提出算法不仅比目前已有的单调性属性约简算法具有更优越的属性约简结果,而且具有更高的约简效率.

Non-monotonic attribute reduction based on neighborhood rough mutual information entropy

Attribute reduction is an important application in rough set theory, and it has been widely used in such areas as machine learning and data mining so far. Neighborhood rough set is a vital method for processing continuous data in rough set theory. For the existed detects of attribute reduction in the current neighborhood rough set model, the model of neighborhood rough entropy based on a neighborhood rough set is defined, meanwhile, the concepts of neighborhood rough combination entropy, neighborhood rough conditional entropy and neighborhood rough mutual information entropy are given, where the neighborhood rough mutual information entropy is an important method for evaluating the correlation of attribute sets, and at the same time, the neighborhood rough mutual information entropy is also proved to has a property of non-monotonic changing, therefore a non-monotonic attribute reduction algorithm based on neighborhood rough mutual information entropy is proposed. The experimental analysis show that the proposed algorithm has not only better results but also higher reduction efficiency than existing monotonic algorithms in attribute reduction.