基于优化可辨识矩阵的多粒度粗糙集属性约简算法
Multi-granularity rough set attribute reduction algorithm based on optimized discernibility matrix
宋苏洋 1叶军 2曾广财 1孙清1
作者信息
- 1. 南昌工程学院信息工程学院,江西 南昌 330000
- 2. 南昌工程学院信息工程学院,江西 南昌 330000;江西省水信息协同感知与智能处理重点实验室,江西 南昌 330000
- 折叠
摘要
为了解决多粒度粗糙集中构造可辨识矩阵计算量过大等问题,提出了一种基于优化可辨识矩阵的改进的多粒度属性约简算法.使用属性重要度作为相似度构造不同粒度空间,输出各粒度空间的优化可辨识矩阵中的核属性,用于求解最终约简,对约简集进行反向冗余检测,避免存在冗余属性.结果表明:该算法能够有效降低时间复杂度,提升约简效率.实例和多个UCI数据集的实验结果验证了该算法的有效性.
Abstract
An improved multi-granularity attribute reduction algorithm based on optimized discernible matrix is proposed to solve the problem of excessive computation for constructing discernible matrix in multi-granularity rough sets.Attribute importance is used as similarity to construct different particle size spaces,and kernel attributes in the optimized discernibility matrix of each particle size space are output to solve the final reduction,and reverse redundancy detection is performed on the reduced set to avoid redundant attributes.The results show that this algorithm can effectively reduce the time complexity and improve the reduction efficiency.Examples and experimental results of several UCI data sets demonstrate the effectiveness of the proposed algorithm.
关键词
多粒度粗糙集/粒度空间/优化可辨识矩阵/属性约简算法Key words
multi-granulation rough sets/granularity spaces/optimized discernibility matrix/attribute reduction algorithm引用本文复制引用
基金项目
江西省教育厅科技项目(GJJ211920)
出版年
2024
NETL
NSTL
万方数据