一种新的决策粗糙集最小化决策代价属性约简算法

徐道磊; 陈培林; 唐轶轩; 吴尚; 路宇; 卞显福

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一种新的决策粗糙集最小化决策代价属性约简算法

更新时间：2026-09-04

- 一种新的决策粗糙集最小化决策代价属性约简算法
- A new decision-theoretic rough set model and minimum decision cost attribute reduction algorithm
- 微电子学与计算机 2020年37卷第8期页码：55-60
- 作者机构：
  
  1. 安徽省电力有限公司信息通信分公司,安徽,合肥,230061
  2. 中国劳动关系学院机构,北京,100028
  3. 中国科学技术大学软件学院,安徽,合肥,230051
- 作者简介：
- 基金信息：
  
  国网合肥供电公司2019年智慧能源“互联网+”服务平台二期实施服务 SGAHHF00XTXX1900770合肥供电公司2019年通信网管系统软硬件运行维护项目 B112A019000Q
- DOI：
  中图分类号：
- 纸质出版：2020
- 稿件说明：
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徐道磊, 陈培林, 唐轶轩, 吴尚, 路宇, 卞显福. 一种新的决策粗糙集最小化决策代价属性约简算法[J]. 微电子学与计算机, 2020, 37(8): 55-60.

XU Dao-lei, CHEN Pei-lin, TANG Yi-xu, et al. A new decision-theoretic rough set model and minimum decision cost attribute reduction algorithm[J]. 2020, 37(8): 55-60.
徐道磊, 陈培林, 唐轶轩, 吴尚, 路宇, 卞显福. 一种新的决策粗糙集最小化决策代价属性约简算法[J]. 微电子学与计算机, 2020, 37(8): 55-60. DOI：

XU Dao-lei, CHEN Pei-lin, TANG Yi-xu, et al. A new decision-theoretic rough set model and minimum decision cost attribute reduction algorithm[J]. 2020, 37(8): 55-60. DOI：

摘要

实际应用中存在着大量的数值型数据，然而传统的决策粗糙集只能够处理符号型数据，为了改善这一局限性，本文构造出一种模糊邻域决策粗糙集模型，并提出一种最小化决策代价的属性约简算法.文中首先将将模糊粗糙集和邻域粗糙集融入决策粗糙集中，提出了模糊邻域决策粗糙集，使得该模型同时具有模糊粗糙集和邻域粗糙集处理数值型数据的优点；然后基于该模型，给出一种决策代价定义，并提出相应的最小化决策代价属性约简算法；最后通过实验分析表明所提出的算法具有较好的代价敏感属性约简性能.

Abstract

There are a lot of numerical data in practical application

but traditional decision-theoretic rough sets can only deal with symbolic data. In order to improve this limitation

this paper constructs a fuzzy neighborhood decision-theoretic rough set model and proposes a minimum decision cost attribute reduction algorithm. Firstly

the fuzzy rough set and neighborhood rough set are integrated into decision-theoretic rough set

and the fuzzy neighborhood decision-theoretic rough set is proposed

which makes the model have the advantages of both fuzzy rough set and neighborhood rough set in dealing with numerical data. Then

based on the model

a definition of decision cost is given and the corresponding minimum decision cost attribute reduction is proposed. Finally

the experimental results show that the proposed algorithm has better cost-sensitive attribute reduction performance.

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references

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