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Data Analysis and Knowledge Discovery  2020, Vol. 4 Issue (1): 40-50    DOI: 10.11925/infotech.2096-3467.2018.1278
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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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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     
Received: 15 November 2018      Published: 14 March 2020
ZTFLH:  TP393  
Corresponding Authors: Yan Ge     E-mail: geyanyan0314137@126.com

Cite this article:

Jie Ma,Yan Ge,Hongyu Pu. Survey of Attribute Reduction Methods. Data Analysis and Knowledge Discovery, 2020, 4(1): 40-50.

URL:

http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2018.1278     OR     http://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/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
Classification of Keywords
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。
Relations and Differences Among Rough Set, Granular Computing and Formal Concept Analysis
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
Comparison of Three Methods of Information Entropy, Difference Matrix and Positive Region
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]
Application Cases of Attribute Reduction
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