关键词: 层次商空间, 模糊欧氏距离, 属性约简

Abstract:

Aiming at the problems that the calculation process of the traditional attribute reduction algorithm by using the equivalent relation is cumbersome and the algorithm takes longer time when the sample set is large, this paper proposes a fast attribute reduction algorithm that uses fuzzy Euclidean distance. Firstly, the fuzzy Euclidean distance is defined to calculate the distance between attributes. Secondly, the hierarchical quotient space structure is used to construct the granular layer space. Finally, the granular space clustering result is used as the basis to reduce the sample set attributes. The simulation results show that the reduction speed of the algorithm is not limited by the number of samples in the sample set, and the operation speed is fast. It can achieve fast reduction of the data without deleting the samples. The reduction has small impact on the classification accuracy of the data set, while the classification accuracy of some data set has been improved, which provides new research ideas for large-scale data set reduction.

Key words: hierarchical quotient space, fuzzy Euclidean distance, attribute reduction