混合数据的邻域区分度增量式属性约简算法

doi:10.3969/j.issn.0372-2112.2020.04.010

电子学报 ›› 2020, Vol. 48 ›› Issue (4): 682-696.DOI: 10.3969/j.issn.0372-2112.2020.04.010

混合数据的邻域区分度增量式属性约简算法

盛魁¹, 王伟³, 卞显福², 董辉¹, 马健¹

1. 亳州职业技术学院信息工程系, 安徽亳州 236800;
2. 中国科学技术大学软件学院, 安徽合肥 230051;
3. 安徽大学计算机科学与技术学院, 安徽合肥 230601

收稿日期:2018-04-13 修回日期:2019-09-25 出版日期:2020-04-25
- 作者简介:
- 盛魁 1981年2月出生,安徽涡阳人.2011年毕业于安徽大学软件学院,随后在亳州职业技术学院信息工程系工作,从事粒计算和数据挖掘方面的研究.E-mail:shengk1981@163.com;卞显福 1981年11月出生,安徽合肥人.2011年毕业于中国科学技术大学软件学院,随后在中国科学技术大学软件学院工作,从事数据挖掘方面的研究.
- 基金资助:
- 安徽省高等学校省级自然科学研究重点基金 (No.KJ2015A417，No.KJ2016A493）; 安徽省高校振兴计划优秀青年人才支持计划 (No.GXYQZD2016529）; 安徽省亳州市产业创新创新团队项目 (亳组[2015]20号-2）

Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data

SHENG Kui¹, WANG Wei³, BIAN Xian-fu², DONG Hui¹, MA Jian¹

1. Department of Information Engineering, Bozhou Vocational and Technical College, Bozhou, Anhui 236800, China;
2. School of Software, University of Science and Technology of China, Hefei, Anhui 230051, China;
3. Computer Science and Technology Institute, Anhui University, Hefei, Anhui 230601, China

Received:2018-04-13 Revised:2019-09-25 Online:2020-04-25 Published:2020-04-25

摘要/Abstract

摘要： 增量式属性约简是一种针对动态环境下的数据挖掘方法.目前已经提出的增量式属性约简算法仅适用于符号型的信息系统，而很少有对混合信息系统进行相关的研究，这促使在混合信息系统下构建相关的增量式属性约简算法.区分度是用于设计属性约简的一种重要方法，本文将传统的区分度在混合信息系统下进行推广，提出邻域区分度的概念，然后分别研究了邻域区分度在混合信息系统下对象增加和对象减少时的增量式学习，最后根据这种增量式学习分别提出了对应的增量式属性约简算法.UCI数据集上的相关实验结果表明，所提出的增量式属性约简比非增量式属性约简能够更快速的更新约简结果.

关键词: 粗糙集, 混合数据, 区分度, 邻域关系, 增量式学习, 属性约简

Abstract: Incremental attribute reduction is a data mining method for dynamic environment.The incremental attribute reduction algorithm already proposed is only applicable to symbolic information systems.However,there are few related studies on mixed information systems,which promotes the construction of the related incremental attribute reduction algorithm under the mixed information system.The discernibility degree is an important method used for designing attribute reduction.In this paper,the traditional discernibility degree is generalized under the mixed information system,and the concept of neighborhood discernibility degree is presented.Then,the incremental learning of neighborhood discernibility degree is studied respectively when objects increase or objects decrease under the mixed information system.Finally,according to this incremental learning,the corresponding incremental attribute reduction algorithms are proposed,respectively.The related experimental results on the UCI data set show that the proposed incremental attribute reduction can update the reduction results more quickly than the non incremental attribute reduction.

Key words: rough set, mixed data, discernibility degree, neighborhood relation, incremental learning, attribute reduction

中图分类号:

TP18

盛魁, 王伟, 卞显福, 董辉, 马健. 混合数据的邻域区分度增量式属性约简算法[J]. 电子学报, 2020, 48(4): 682-696.

SHENG Kui, WANG Wei, BIAN Xian-fu, DONG Hui, MA Jian. Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data[J]. Acta Electronica Sinica, 2020, 48(4): 682-696.

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混合数据的邻域区分度增量式属性约简算法

Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data

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