电子学报 ›› 2019, Vol. 47 ›› Issue (5): 1111-1120.DOI: 10.3969/j.issn.0372-2112.2019.05.019

• 学术论文 • 上一篇    下一篇

属性约简簇的优化选择

邓大勇1,2, 葛雅雯2, 黄厚宽3   

  1. 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
  • 收稿日期:2018-06-10 修回日期:2018-09-06 出版日期:2019-05-25 发布日期:2019-05-25
  • 作者简介:邓大勇 男,1968年出生,副教授,博士,现为浙江师范大学行知学院教师,主要研究方向为粗糙集、粒计算、数据挖掘等.E-mail:dayongd@163.com;葛雅雯 女,1992年生,硕士研究生.主要研究方向为粗糙集、数据挖掘.E-mial:541865527@qq.com;黄厚宽 男,1940年生,教授,博士生导师.主要方向为计算智能、数据挖掘和粗糙集等.E-mial:hkhuang@bjtu.edu.cn
  • 基金资助:
    国家自然科学基金(No.61473030);浙江省自然科学基金(No.LY15F020012);浙江师范大学网络空间安全浙江省一流学科

An Optimizing Selection in a Family of Attribute Reducts

DENG Da-yong1,2, GE Ya-wen2, HUANG Hou-kuan3   

  1. 1. Xingzhi College, Zhejiang Normal University, Jinhua, Zhejiang 321004, China;
    2. College of Mathematics, Physics and Information Engineering, Zhejiang Normal University, Jinhua, Zhejiang 321004, China;
    3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2018-06-10 Revised:2018-09-06 Online:2019-05-25 Published:2019-05-25

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.

关键词: 粗糙集, 属性约简, 概念漂移, 属性约简重心

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.

Key words: rough sets, attribute reduction, concept drift, focus of attribute reducts

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