An Optimizing Selection in a Family of Attribute Reducts

DENG Da-yong; GE Ya-wen; HUANG Hou-kuan

doi:10.3969/j.issn.0372-2112.2019.05.019

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An Optimizing Selection in a Family of Attribute Reducts

更新时间：2025-07-16

- An Optimizing Selection in a Family of Attribute Reducts
- Acta Electronica Sinica Vol. 47, Issue 5, Pages: 1111-1120(2019)
- 作者机构：
  
  1. 浙江师范大学行知学院,浙江,金华,321004
  2. 浙江师范大学数理与信息工程学院,浙江,金华,321004
  3. 北京交通大学计算机与信息技术学院,北京,100044
  4. 浙江师范大学行知学院,浙江,金华,321004
  5. 浙江师范大学数理与信息工程学院,浙江,金华,321004
  6. 北京交通大学计算机与信息技术学院,北京,100044
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2019.05.019
  CLC： TP18
- Published Online：25 May 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
DENG Da-yong, GE Ya-wen, HUANG Hou-kuan. An Optimizing Selection in a Family of Attribute Reducts[J]. Acta Electronica Sinica, 2019, 47(5): 1111-1120.
DOI：

DENG Da-yong, GE Ya-wen, HUANG Hou-kuan. An Optimizing Selection in a Family of Attribute Reducts[J]. Acta Electronica Sinica, 2019, 47(5): 1111-1120. DOI： 10.3969/j.issn.0372-2112.2019.05.019.

摘要

属性约简是粗糙集的一个重要应用.一个数据集往往含有多个属性约简，人们一般用启发式算法找到其中的一个，再通过实验的方法验证其有效性.面对多个属性约简，人们往往难以区别，缺乏有效的手段选取最优或较优的属性约简.使用多种概念漂移的度量指标和信息损失的度量方法比较了同一个知识系统中不同Pawlak约简之间的区别与联系.提出了属性约简重心的概念，并研究其性质.实验结果显示，在众多的属性约简中，离重心最近的属性约简在分类准确率方面具有较大的优势.概念漂移的度量指标和信息损失的度量方法有助于区分不同的属性约简，属性约简的重心有助于在众多的属性约简中选择最优或较优的一个.

Abstract

Attribute reduction is one of important applications in rough set theory.There are more than one attribute reduct in a data set

and heuristic algorithms are always used to find one of them

which is verified with experiments.For many attribute reducts

it is hard for people to distinguish them

and lacks of valid methods of selecting the best one or a better one.Indexes of concept drift and information loss are employed to compare the same type of Pawlak attribute reducts in a knowledge system.The focus of attribute reducts is presented

and its properties are investigated in this paper.Experimental results show that the closest attribute reduct to the focus of attribute reducts is better than other attribute reducts in classification accuracy.Indexes of concept drift detection and information loss can distinguish different attribute reducts

and the focus of attribute reducts can be employed to select the best attribute reduct or a better one.

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