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数据分析与知识发现  2020, Vol. 4 Issue (1): 40-50     https://doi.org/10.11925/infotech.2096-3467.2018.1278
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属性约简方法研究综述*
马捷1,2,葛岩1,3(),蒲泓宇1
1吉林大学管理学院 长春 130022
2吉林大学信息资源研究中心 长春 130022
3北华大学计算机科学技术学院 吉林 132021
Survey of Attribute Reduction Methods
Jie Ma1,2,Yan Ge1,3(),Hongyu Pu1
1School of Management, Jilin University, Changchun 130022, China
2Center for Information Resources Research, Jilin University, Changchun 130022, China
3School of Computer Science and Technology, Beihua University, Jilin 132021, China
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摘要 

【目的】 探讨属性约简方法的发展趋势及应用领域,为该领域的系统研究提供借鉴。【文献范围】 在Web of Science和CNKI中分别以检索词“Attribute Reduction”和“属性约简”进行文献检索,再结合主题筛选,精读并使用追溯法获得属性约简研究的代表性文献共142篇。【方法】 介绍属性约简的基本方法,对属性约简方法的主要研究内容进行归类总结。【结果】 属性约简方法的热点研究集中在利用粗糙集、粒计算和形式概念分析等基本方法,其发展趋势与数据的动态性、智能算法之间的相互融合密切相关。【局限】 仅针对属性约简算法之间的融合发展进行简要论述, 未对其进行更深入探讨。【结论】 多种属性约简算法的融合研究是属性约简算法的发展趋势。

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马捷
葛岩
蒲泓宇
关键词 粗糙集粒计算形式概念分析    
Abstract

[Objective] This paper reviews the methods, developing trends and applications of attribute reduction, aiming to support systematic research in this field.[Coverage] From the Web of Science and CNKI, we retrieved 142 articles on attribute reduction, using the keywords of “Attribute Reduction” and “属性约简”. We also optimized the results with topic selection, intensive reading and retrospective method.[Methods] We surveyed the fundamentals of attribute reduction, and then summarized its leading research.[Results] The popular research of attribute reduction methods focused on rough sets, granular computing and formal concept analysis. Its developing trends were closely related to the dynamics of data and the fusion of intelligent algorithms.[Limitations] We only briefly discussed the merging of attribute reduction algorithms.[Conclusions] We explored the developing trends of attribute reduction methods.

Key wordsRough Set    Granular Computing    Formal Concept Analysis
收稿日期: 2018-11-15      出版日期: 2020-03-14
ZTFLH:  TP393  
基金资助:*本文系国家社会科学基金重点项目“信息生态视角下智慧城市信息协同结构与模式研究”的研究成果之一(17ATQ007)
通讯作者: 葛岩     E-mail: geyanyan0314137@126.com
引用本文:   
马捷,葛岩,蒲泓宇. 属性约简方法研究综述*[J]. 数据分析与知识发现, 2020, 4(1): 40-50.
Jie Ma,Yan Ge,Hongyu Pu. Survey of Attribute Reduction Methods. Data Analysis and Knowledge Discovery, 2020, 4(1): 40-50.
链接本文:  
https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/10.11925/infotech.2096-3467.2018.1278      或      https://manu44.magtech.com.cn/Jwk_infotech_wk3/CN/Y2020/V4/I1/40
类别 关键词 类型定义
1 粗糙集、差别矩阵、粗糙集理论、信息熵、邻域粗糙集、条件熵、模糊粗糙集、区分矩阵、决策粗糙集、不完备决策表、正区域、变精度粗糙集、正域、辨识矩阵、算法复杂度、启发式算法、序信息系统、互信息、信息量、布尔矩阵、分辨矩阵、依赖度、模糊集、区间值信息系统、相似关系、粗集、邻域关系、条件信息熵、相似度、覆盖粗糙集 基于粗糙集的属性约简方法
2 属性重要度、约简、优势关系、属性重要性、粒计算、知识粒度、约简核、多粒度、容差关系、相容关系、属性约简算法、粒子群、属性核、冲突域、模糊等价关系、多粒度粗糙集 基于粒计算的属性约简方法
3 决策表、不完备信息系统、概念格、形式背景、决策系统、属性依赖度、决策规则、决策树、邻域、决策形式背景、不完备形式背景、不完备决策系统、属性、区分能力、不可约元 基于形式概念分析的属性约简方法
4 遗传算法、信息系统、数据挖掘、故障诊断、支持向量机、规则提取、集值信息系统、属性特征、蚁群算法、增量式学习、MapReduce、算法、增量学习、蚁群优化、规则获取、增量式、神经网络、分布约简、不一致决策表、包含度、二叉树、依赖空间 基于智能算法的属性约简方法
Table 1  关键词类别划分
方法 信息表示 处理变量 基本组成 划分问题 数据分类的依据 特点
粗糙集 一个子集 名义型变量 上近似集、
下近似集
通过属性的增加或删减来控制 等价关系 研究离散的对象集,针对属性值的差异对对象进行分类,从而形成集合的上、下近似,而对象之间没有结构关系或拓扑关系。
粒计算 粒子 数值型数据 粒子、粒层、
粒结构
在不同粒层之间相互转化 基于等价关系、基于模糊集等 粒计算是通用的结构化问题求解方法。
形式概念分析 一个概念 名义型变量、数值型数据 对象集、属性集 改变概念的内涵 序理论、格理论 对概念进行分层处理。
Table 2  粗糙集、粒计算与形式概念分析三者之间的联系与区别
方法 优点 缺点 适用范围
信息熵 信息熵度量不确定性数据 条件属性较多时,时间复杂度高 适用于存在较多不确定因素的数据集
差别矩阵 方法简单、解释性良好 差别矩阵中可能出现重复元素 适用于较小数据集
正区域 对等价类进行划分,降低了时间和空间复杂度 等价类划分需要依据相应方法,否则分类的准确性不高 适用于包含少量条件属性的相对属性约简
Table 3  信息熵、差别矩阵、正区域三种方法的比较
应用领域 目的
医学领域 处理海量电子病历[70,71]
商业领域 提取销售规则[72,73]
教育领域 教学质量评价[74]、图书馆信息资源评价[75]
应急管理领域 获取突发事件的关联规则[76]、辅助救援调度决策[77,78]
评价问题 矿井火灾风险评价[79]、云计算安全风险评估[80]、企业竞争力指标体系构建[81]
Table 4  属性约简的应用案例
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