动态信息系统中基于序贯三支决策的属性约简方法

计算机科学 ›› 2019, Vol. 46 ›› Issue (6A): 120-123.

动态信息系统中基于序贯三支决策的属性约简方法

李艳^1,2, 张丽², 陈俊芬²

北京师范大学珠海分校应用数学学院广东珠海519087¹;
河北大学数学与信息科学学院河北省机器学习与计算智能重点实验室河北保定071002²

出版日期:2019-06-14 发布日期:2019-07-02
通讯作者: 李艳(1976-),女,博士,教授,CCF会员,主要研究方向为机器学习、Rough集理论、计算智能,E-mail:ly@hbu.cn
作者简介:张丽(1990-),女,硕士生,主要研究方向为粒计算与知识发现,E-mail:1037277907@qq.com;陈俊芬(1976-),女,博士,副教授,主要研究方向为机器学习、图像处理。
基金资助:
本文受NSFC(61473111),河北省自然科学基金(F2018201096,F2016201161),河北大学自然科学研究计划项目(799207217069),北京师范大学珠海分校教师科研能力促进计划资助。

Attribute Reduction Method Based on Sequential Three-way Decisions in Dynamic Information Systems

LI Yan^1,2, ZHANG Li², CHEN Jun-fen²

School of Applied Mathematics,Beijing Normal University,Zhuhai,Zhuhai,Guangdong 519087,China¹;
Key Lab of Machine Learning and Computational Intelligence,College of Mathematics and Information Science,;
Hebei University,Baoding,Hebei 071002,China²

Online:2019-06-14 Published:2019-07-02

摘要/Abstract

摘要： 针对多准则分类问题,即条件属性为有序的符号值或连续值,而决策属性为类别标签的问题,采用优势-等价关系来表示其信息系统。但很多现实中的信息系统又是动态变化的,属性约简作为其重要的知识需要及时更新。为处理带有偏好关系的动态信息系统,建立多标准决策问题中的高效知识更新方法,提出了优势-等价关系下基于序贯三支决策的约简更新方法。将多粒度结合起来形成动态粒序,当对象集和属性集变化时通过重用原有信息快速更新属性约简,从而降低知识更新的代价。最后选取了多组UCI数据集进行实验,结果表明所提方法能够在保证约简质量的基础上明显降低计算耗费。

关键词: 动态信息系统, 序贯三支决策, 优势关系, 知识更新, 属性约简

Abstract: Multi-criteria classification problem refers to a type of classification problem which has ordered-valued conditional attributes.The dominance-equivalence relation is used to describe information systems of this kind of problems.However,many real-world information systems are dynamic,attribute reductions need to be often updated as the most important knowledge in decision making.In order to deal with the dynamic information system with preference relations and provide an efficient method for updating attribute reductions for multi-criterion decision-making problems,this paper established an efficient knowledge updating method based on sequential three-way decisions under dominance-equivalence relations.Multi-granules are combined to form dynamic granular sequence,the attribute reduction are updated through reusing current information when the object set or attribute set change,thus saving the cost of attribute reduction process.Several UCI datasets are selected for experiments.The results show that the proposed method can reduce the time consumption noticeably when guarantee the quality of the attribute reduction.

Key words: Attribute reduction, Dominance relation, Dynamic information system, Knowledge updating, Sequential three-way decisions

中图分类号:

TP181

李艳, 张丽, 陈俊芬. 动态信息系统中基于序贯三支决策的属性约简方法[J]. 计算机科学, 2019, 46(6A): 120-123. https://doi.org/

LI Yan, ZHANG Li, CHEN Jun-fen. Attribute Reduction Method Based on Sequential Three-way Decisions in Dynamic Information Systems[J]. Computer Science, 2019, 46(6A): 120-123. https://doi.org/

参考文献

[1]YAO Y.An outline of a theory of three-way decisions[C]∥In-ternational Conference on Rough Sets and Current Trends in Computing.Springer,Berlin,Heidelberg,2012:1-17.
[2]YAO Y.Rough sets and three-way decisions [M].Rough Sets and Knowledge Technology.Springer International Publishing,2015.
[3]YAO Y.Decision-theoretic rough set models[C]∥2nd International Conference on Rough Sets and Knowledge Technology.2007:1-12.
[4]LIU D,YAO Y,LI T.Three-way investment decisions with decision-theoretic rough sets [J].International Journal of Computational Intelligence Systems,2011,4(1):66-74.
[5]LINGRAS P,CHEN M,MIAO D.Rough cluster quality index based on decision theory [J].IEEE Transactions on Knowledge and Data Engineering,2009,21(7):1014-1026.
[6]YU H,LIU Z,WANG G.An automatic method to determine the number of clusters using decision-theoretic rough set [M].Elsevier Science Inc.,2014.
[7]LI H,ZHANG L,HUANG B,et al.Sequential three-way decision and granulation for cost-sensitive face recognition [J].Knowledge-Based Systems,2016,91:241-251.
[8]JIA X,SHANG L,ZHOU B,et al.Generalized attribute reduct in rough set theory [J].Knowledge-Based Systems,2016,91:204-218.
[9]MENG Z,SHI Z.On quick attribute reduction in decision-theoretic rough set models [J].Information Sciences,2016,330:226-244.
[10]LI J,HUANG C,QI J,et al.Three-way cognitive concept lear-ning via multi-granularity [J].Information Sciences,2017,378(1):244-263.
[11]LI J,MEI C,XU W,et al.Concept learning via granular computing:a cognitive viewpoint [J].Information Sciences,2015,298(1):447-467.
[12]YAO Y.Granular computing and sequential three-way decisions[C]∥RSKT 2013.2013:16-27.
[13]QIAN J,DANG C,YUE X,et al.Attribute reduction for se-quential three-way decisions under dynamic granulation[J].International Journal of Approximate Reasoning,2017,85:196-216.
[14]GRECO S,MATARAZZZO B,SLOWINSKI R.Rough approximation by dominance relations[J].International Journal of Intelligent Systems,2002,17(2):153-171.
[15]陈娟,王国胤,胡军.优势关系下不协调信息系统的正域约简[J].计算机科学,2008,35(3):216-2181.
[16]李艳,孙娜欣,赵津,等.基于优势-等价关系的几种约简及规则抽取方法[J].计算机科学,2011,38(11):220-224.
[17]JIN Y,LI Y,HE Q.A fast positive-region reduction method based on dominance-equivalence relations [C]∥International Conference on Machine Learning and Cybernetics.IEEE,2017:152-157.
[18]LIANG J,WANG F,DANG C,et al.A group incremental approach to feature selection applying rough set technique[J].IEEE Transactions on Knowledge & Data Engineering,2014,26(2):294-308.
[19]LI S,LI T,LIU D.Dynamic maintenance of approximations in dominance-based rough set approach under the variation of the object set[J].International Journal of Intelligent Systems,2013,28(8):729-751.
[20]LUO C,LI T,CHEN H,et al.Incremental approaches for updating approximations in set-valued ordered information systems[J].Knowledge-Based Systems,2013,50(50):218-233.
[21]LIANG J,WANG F,DANG C,et al.A group incremental approach to feature selection applying rough set technique[J].IEEE Transactions on Knowledge & Data Engineering,2014,26(2):294-308.
[22]YANG Y,CHEN D,WANG H.Active sample selection based incremental algorithm for attribute reduction with rough sets[J].IEEE Transactions on Fuzzy Systems,2017,25(4):825-838.
[23]LI Y,LIU X Q,HOU J J.Incrementally updating method in dominance-based rough set approach[J].Int. J. Intell. Inf. Manag. Sci.,2014,3(1):18-22.
[24]BACHE K,LICHMA M.UCI Machine Learning Repository [OL].http://archive.ics.uci.edu/ml.

相关文章 15

[1]	王子茵, 李磊军, 米据生, 李美争, 解滨. 基于误分代价的变精度模糊粗糙集属性约简 Attribute Reduction of Variable Precision Fuzzy Rough Set Based on Misclassification Cost 计算机科学, 2022, 49(4): 161-167. https://doi.org/10.11896/jsjkx.210500211
[2]	王志成, 高灿, 邢金明. 一种基于正域的三支近似约简 Three-way Approximate Reduction Based on Positive Region 计算机科学, 2022, 49(4): 168-173. https://doi.org/10.11896/jsjkx.210500067
[3]	李艳, 范斌, 郭劼, 林梓源, 赵曌. 基于k-原型聚类和粗糙集的属性约简方法 Attribute Reduction Method Based on k-prototypes Clustering and Rough Sets 计算机科学, 2021, 48(6A): 342-348. https://doi.org/10.11896/jsjkx.201000053
[4]	曾惠坤, 米据生, 李仲玲. 形式背景中概念及约简的动态更新方法 Dynamic Updating Method of Concepts and Reduction in Formal Context 计算机科学, 2021, 48(1): 131-135. https://doi.org/10.11896/jsjkx.200800018
[5]	薛占熬, 张敏, 赵丽平, 李永祥. 集对优势关系下多粒度决策粗糙集的可变三支决策模型 Variable Three-way Decision Model of Multi-granulation Decision Rough Sets Under Set-pair Dominance Relation 计算机科学, 2021, 48(1): 157-166. https://doi.org/10.11896/jsjkx.191200175
[6]	桑彬彬, 杨留中, 陈红梅, 王生武. 优势关系粗糙集增量属性约简算法 Incremental Attribute Reduction Algorithm in Dominance-based Rough Set 计算机科学, 2020, 47(8): 137-143. https://doi.org/10.11896/jsjkx.190700188
[7]	岳晓威, 彭莎, 秦克云. 基于面向对象(属性)概念格的形式背景属性约简方法 Attribute Reduction Methods of Formal Context Based on ObJect (Attribute) Oriented Concept Lattice 计算机科学, 2020, 47(6A): 436-439. https://doi.org/10.11896/JsJkx.191100011
[8]	陈毅宁,陈红梅. 基于距离比值尺度的模糊粗糙集属性约简 Attribute Reduction of Fuzzy Rough Set Based on Distance Ratio Scale 计算机科学, 2020, 47(3): 67-72. https://doi.org/10.11896/jsjkx.190100196
[9]	徐怡,唐静昕. 基于优化可辨识矩阵和改进差别信息树的属性约简算法 Attribute Reduction Algorithm Based on Optimized Discernibility Matrix and Improving Discernibility Information Tree 计算机科学, 2020, 47(3): 73-78. https://doi.org/10.11896/jsjkx.190500125
[10]	侯成军,米据生,梁美社. 基于局部可调节多粒度粗糙集的属性约简 Attribute Reduction Based on Local Adjustable Multi-granulation Rough Set 计算机科学, 2020, 47(3): 87-91. https://doi.org/10.11896/jsjkx.190500162
[11]	郭庆春,马建敏. 对偶区间集概念格上区间集协调集的判定方法 Judgment Methods of Interval-set Consistent Sets of Dual Interval-set Concept Lattices 计算机科学, 2020, 47(3): 98-102. https://doi.org/10.11896/jsjkx.190500098
[12]	龙柄翰, 徐伟华, 张晓燕. 不协调目标信息系统中基于改进差别信息树的分布属性约简 Distribution Attribute Reduction Based on Improved Discernibility Information Tree in Inconsistent System 计算机科学, 2019, 46(6A): 115-119.
[13]	李艳, 张丽, 王雪静, 陈俊芬. 优势-等价关系下序贯三支决策的属性约简 Attribute Reduction for Sequential Three-way Decisions Under Dominance-Equivalence Relations 计算机科学, 2019, 46(2): 242-148. https://doi.org/10.11896/j.issn.1002-137X.2019.02.037
[14]	姜泽华, 王怡博, 徐刚, 杨习贝, 王平心. 面向多尺度的属性约简加速器 Multi-scale Based Accelerator for Attribute Reduction 计算机科学, 2019, 46(12): 250-256. https://doi.org/10.11896/jsjkx.181102031
[15]	李仲玲, 米据生, 解滨. 不协调决策形式背景的属性约简 Attribute Reduction in Inconsistent Decision Formal Contexts 计算机科学, 2019, 46(12): 257-260. https://doi.org/10.11896/jsjkx.181102137

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed