基于区分矩阵的多粒度属性约简

多粒度是粒计算领域的重要研究方向之一,它在两个或多个不同的粒度下进行问题求解,已经成为解决复杂问题的一种新的范式。属性约简作为粗糙集理论的核心内容之一,已被成功地应用于粒计算、数据挖掘等领域。将多粒度思想应用于属性约简将是一个有意义的研究方向。为此,本文运用粒计算理论中的粒化思想进行属性粒化,构造多个属性粒;然后基于属性粒上的区分矩阵计算属性粒的重要度和属性粒中属性重要度;最后利用这两种重要度设计了一种多粒度属性约简算法。通过在不同的粒中挑选属性,该算法得到的约简结果更具有代表性和差异性。本文利用6个数据集对提出的多粒度属性约简算法的性能进行测试,实验结果表明了提出算法的有效性。

Multi-granulation Attribute Reduction Based on Discernibility Matrix

Multi-granularity is one of the important research directions in the field of granular computing. It represents the research of problem solving at two or more different granules and already has become a new computing method to solve complex problems. Rough set theory is a kind of computing tool to solve uncertain problems effectively. As one of the core contents of rough set theory, attribute reduction has been widely studied in the fields of data mining, machine learning, and granular computing. It is a meaningful problem to apply the idea of multi-granularity to attribute reduction. The granulation idea in granular computing theory is used to granulate attributes to form multiple granules. Then we evaluate the significance of attribute granules and the significance of attributes in attribute granules based on discernibility matrix. Finally, a multi-granularity attribute reduction algorithm based on these two significance measures is designed. By selecting attributes from different granules, the reduction results obtained by this algorithm are more representative and different. In order to verify the effectiveness of our proposed method, experiments on six data sets show that the effectiveness of the proposed algorithm.