结合分辨矩阵改进的邻域粗糙集属性约简算法

doi:10.6040/j.issn.1672-3961.0.2021.487

山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (4): 99-109.doi: 10.6040/j.issn.1672-3961.0.2021.487

• • 上一篇

结合分辨矩阵改进的邻域粗糙集属性约简算法

季雨瑄¹,叶军^1,2*,杨震宇¹,敖家欣¹,王磊^1,2

1.南昌工程学院信息工程学院, 江西南昌 330000;2.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

发布日期:2022-08-24
作者简介:季雨瑄(1998— ),女,江苏南京人,硕士研究生,主要研究方向为粗糙集和数据挖掘. E-mail: 1003528134@qq.com. *通信作者简介:叶军(1968— ),男,江西万安人,教授,硕士,主要研究方向为粗糙集和数据挖掘. E-mail: 2003992646@nit.edu.cn
基金资助:
江西省教育厅科技项目(GJJ211920,GJJ170995);国家自然科学基金项目(61562061)

An improved neighborhood rough set attribute reduction algorithm combined with resolution matrix

JI Yuxuan¹, YE Jun^1,2*, YANG Zhenyu¹, AO Jiaxin¹, WANG Lei^1,2

1. School of Information Engineering, Nanchang Institute of Technology, Nanchang 330000, Jiangxi, China;
2. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing, Nanchang 330000, Jiangxi, China

Published:2022-08-24

摘要/Abstract

摘要： 针对基于邻域粗糙集属性重要度约简算法在某些决策表中约简正确率下降等问题,结合基于等价关系下的分辨矩阵知识,定义一种邻域决策系统下的分辨矩阵,邻域分辨矩阵由能够分辨不同邻域对象的条件属性子集组成。根据条件属性在邻域分辨矩阵中的占比提出一种属性重要度的度量方法,以新的重要度作为启发性因子,设计一种邻域决策系统下属性重要度启发性约简算法。该算法以核属性集作为初始集合,依次选择重要度大的属性加入到核集,直至找到最小属性约简时,算法终止。实例分析和UCI数据集试验结果表明,与基于属性依赖度的约简算法相比,该算法能够更有效地找到最小属性约简集,并且可以有效减少计算工作量,证明了算法的有效性和可实用性。

关键词: 邻域决策系统, 重要度, 属性约简, 邻域分辨矩阵, 最小约简集

中图分类号:

TP18

季雨瑄,叶军,杨震宇,敖家欣,王磊. 结合分辨矩阵改进的邻域粗糙集属性约简算法[J]. 山东大学学报 (工学版), 2022, 52(4): 99-109.

JI Yuxuan, YE Jun, YANG Zhenyu, AO Jiaxin, WANG Lei. An improved neighborhood rough set attribute reduction algorithm combined with resolution matrix[J]. Journal of Shandong University(Engineering Science), 2022, 52(4): 99-109.

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结合分辨矩阵改进的邻域粗糙集属性约简算法

An improved neighborhood rough set attribute reduction algorithm combined with resolution matrix

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