广义不完备混合决策系统的知识约简

赵佰亭; 陈希军; 曾庆双

您当前的位置：

首页 >

文章列表页 >

广义不完备混合决策系统的知识约简

信息工程 | 更新时间：2023-11-28

- 广义不完备混合决策系统的知识约简
- Approach to Knowledge Reduction in Generalized Incomplete Hybrid Decision System
- 工程科学与技术 2009年41卷第6期页码：177-182
- 作者机构：
  
  1. 哈尔滨工业大学空间控制与惯性技术研究中心
  2. 哈尔滨工业大学空间控制与惯性技术研究中心黑龙江,哈尔滨,150001
- 作者简介：
- 基金信息：
  
  国防科技预研基金项目（9140A17030207HT0150）,十一五总装备部预言基金资助项目(51309030102)
- DOI：
  中图分类号：
- 纸质出版日期：2009，
  
  网络出版日期：2009-4-21，
扫描看全文
赵佰亭,陈希军,曾庆双.广义不完备混合决策系统的知识约简[J].工程科学与技术,2009,41(6):177-182.

Zhao Bai-Ting, 陈希军, 曾庆双. Approach to Knowledge Reduction in Generalized Incomplete Hybrid Decision System[J]. Advanced Engineering Sciences, 2009,41(6):177-182.
赵佰亭,陈希军,曾庆双.广义不完备混合决策系统的知识约简[J].工程科学与技术,2009,41(6):177-182. DOI：

Zhao Bai-Ting, 陈希军, 曾庆双. Approach to Knowledge Reduction in Generalized Incomplete Hybrid Decision System[J]. Advanced Engineering Sciences, 2009,41(6):177-182. DOI：

摘要

中文摘要: 针对现实中同时具有丢失型和遗漏型未知属性的混合决策系统的约简问题，建立了广义不完备邻域粗糙集模型，提出了未知属性的辨别方法，给出了一种混合约简算法。模型采用广义邻域关系度量不可分辨关系，通过邻域信息粒子逼近论域空间，是非对称相似关系和容差关系的广义化。依据分类一致性假设及广义邻域关系进行未知属性的辨别，讨论了噪声样本和邻域大小对分类精度的影响。采用UCI数据库中5组数据进行了仿真试验，预测精度证明了约简算法的有效性和可行性。

Abstract

Abstract:In order to reduce the hybrid decision systems including the missing value attributes

which are lost or represent “do not care” conditions

a generalized incomplete rough set model based on neighborhood relations

the discrimination methods of the missing value and a hybrid reduction algorithm were proposed. The model approximates an arbitrary subset in the universe with neighborhood granules

and the generalized neighborhood relations are the generalization of the asymmetry similarity relations and the tolerance relations. The discrimination methods of the lost or “do not care” conditions were proposed based on the assumption of the consistency classification

and the influence of the noise samples and the neighborhood values to the classification accuracy was presented as well. The validity and feasibility of the algorithm were demonstrated by the results of experiments on five UCI machine learning databases.

关键词

广义不完备混合决策系统邻域粗糙集约简

Keywords

generalized incompletehybrid decision systemneighborhoodrough setreduction

references

Qinghua Hu ；Jinfu Liu ；Daren Yu,Mixed feature selection based on granulation and approximation,Knowledge-Based Systems ,2008, 21(4).

胡清华,于达仁,谢宗霞.基于邻域粒化和粗糙逼近的数值属性约简[J].软件学报,2008(3)doi:10.3724/SP.J.1001.2008.00640

Yao YY.,Relational interpretations of neighborhood operators and rough set approximation operators,Information Sciences ,1998, 111(1/4).

Lin T Y;Liu Q;Huang K J,Rough sets neighborhood systems and approximation,Selected Papers,1990.

叶玉玲;伞冶.一种混合决策系统属性约简算法研究[J].系统仿真学报,2007(13)

Grzymala-Busse J W,Data with missing attribute values:generalization of indiscernibility relation and rule induction,Berlin:Spriuger-Verlag,2004.

Kryszkiewicz M,Comparative study of alternative types of knowledge reduction in inconsistent systems,International Journal of Intelligent Systems,2001.

Jerzy Stefanowski ；Alexis Tsoukias,Incomplete information tables and rough classification,Computational Intelligence ,2001, 17(3).

Grzymala-Busse J W,On the unknown attribute values in learning from examples,Berlin,Heiddberg:Springer-Verlag,New York,1991.

Grsymala-Busse J W;Wang A Y,Modified algorithms LEMI and LEM2 for ride induction from data with missing attribute values,Research Triangle Park,NC,1997.

Wu Z;Zhang W X,Neighborhood operator systems and approximations,Information Sciences,2002(1-4).

Pawlak Z,Rough sets,International Journal of Computer and Information Sciences,1982(5).

浏览量

212

下载量

CNKI被引量

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于简化差别矩阵的增量式属性约简

水平划分决策表的属性约简算法