信息观下基于不一致邻域矩阵的属性约简

信息观下研究邻域决策系统的属性约简是一种新颖的思路. 通过分析论域下某样本邻域中其他样本与该样本决策属性值的异同, 定义不一致邻域矩阵. 在计算属性重要度时, 利用不一致邻域减少在原条件属性基础上增加一个属性后条件熵的计算时间. 分析得到邻域系统下条件熵与正域的关系, 提出一种信息观下基于不一致邻域矩阵的属性约简算法, 并分析该算法与其他算法的内在联系. 实验结果验证了所提出算法的有效性.

Attribute reduction based on inconsistent neighborhood matrix under information view

It is a new approach for attribute reduction in the neighborhood decision system from the viewpoint of information theory. By analyzing the decision attribute values of samples in neighborhood, the inconsistent neighborhood matrix is defined. The inconsistent neighborhood matrix can be used to narrow the research range while adding more attributes with respect to existing condition attributes. Therefore, it is faster to calculate the significance of attributes by means of condition entropy. The relationship between the conditional entropy and the positive region is found under the neighborhood systems. An attribute reduction algorithm based on inconsistent neighborhood matrix is proposed, and the internal connection between this algorithm and other algorithms is analyzed. The experiment verifies the effectiveness of the proposed algorithm.