Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data

doi:10.3969/j.issn.0372-2112.2020.04.010

您当前的位置：

首页 >

文章列表页 >

Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data

更新时间：2025-07-08

- Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data
- Acta Electronica Sinica Vol. 48, Issue 4, Pages: 682-696(2020)
- 作者机构：
  
  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
  4. 亳州职业技术学院信息工程系,安徽,亳州,236800
  5. 中国科学技术大学软件学院,安徽,合肥,230051
  6. 安徽大学计算机科学与技术学院,安徽,合肥,230601
- 作者简介：
- 基金信息：
  
  Key Fund for Provincial Natural Science Research Project of Universities in Anhui Province (No.KJ2015A417, No.KJ2016A493);Anhui Higher Education Revitalization Program - Program for Distinguished Young Talents in Higher Education of Anhui Province (No.GXYQZD2016529);Haozhou Industrial Innovation Team Project of Anhui Province (亳组[2015]20号-2)
- DOI：10.3969/j.issn.0372-2112.2020.04.010
  CLC： TP18
- Published Online：25 April 2020，
  
  Published：2020
- 稿件说明：
移动端阅览
Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data[J]. Acta Electronica Sinica, 2020, 48(4): 682-696.
DOI：

Neighborhood Discernibility Degree Incremental Attribute Reduction Algorithm for Mixed Data[J]. Acta Electronica Sinica, 2020, 48(4): 682-696. DOI： 10.3969/j.issn.0372-2112.2020.04.010.

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.

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.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Survey on Attribute Reduction Algorithm of Rough Set

Attribute Decision Reduction Method Based on Hybrid Rough Sets and Niche Immune Optimization

Boundary Thinking for Cognitive Uncertainty Problems

Knowledge Acquisition from Decision Tables Containing Continuous-Valued Attributes

Related Author

ZHOU Tao

LU Hui-ling

HUO Bing-qiang

REN Hai-ling

XU Xue-song

CHEN Rong-yuan

WANG Guo-yin

CHENG Yun-long

Related Institution

School of Computer Science and Engineering， North Minzu University

School of Science， Ningxia Medical University

Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission， North Minzu University

The First People’s Hospital of Yinchuan City

College of Computer and Information Engineering Hunan University of Commerce Changsha Hunan China

⁰