计算机科学 ›› 2019, Vol. 46 ›› Issue (2): 242-148.doi: 10.11896/j.issn.1002-137X.2019.02.037

• 人工智能 • 上一篇    下一篇

优势-等价关系下序贯三支决策的属性约简

李艳1,2, 张丽1, 王雪静1, 陈俊芬1   

  1. 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 0710021
    北京师范大学珠海分校应用数学学院 广东 珠海5190872
  • 收稿日期:2018-02-08 出版日期:2019-02-25 发布日期:2019-02-25
  • 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;王雪静(1994-),女,硕士生,主要研究方向为粒计算与知识发现;陈俊芬(1976-),女,博士,副教授,主要研究方向为计算智能、图像处理。
  • 基金资助:
    本文受国家自然科学基金(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 for Sequential Three-way Decisions Under Dominance-Equivalence Relations

LI Yan1,2, ZHANG Li1, WANG Xue-jing1, CHEN Jun-fen1   

  1. Key Lab of Machine Learning and Computational Intelligence,College of Mathematics and Information Science,Hebei University,Baoding,Hebei 071002,China1
    School of Applied Mathematics,Beijing Normal University,Zhuhai,Zhuhai,Guangdong 519087,China2
  • Received:2018-02-08 Online:2019-02-25 Published:2019-02-25

摘要: 序贯三支决策方法是一种能够表示问题中的多重层次粒度,并将多粒度结合起来解决不确定决策问题的有效途径。优势-等价关系粗糙集则是针对条件属性具有偏好关系的分类问题,提取有序信息,对目标概念进行近似,从而形成决策知识。利用传统的优势关系粗糙集方法进行知识约简和提取的效率低下,而目前大部分序贯三支决策方法则局限在符号值属性的信息系统中,对连续值和有序值不能进行有效处理,造成一定程度的信息丢失。因此,将序贯三支决策的思想应用于优势关系粗糙集模型中,定义了一种新的基于序贯三支决策的属性约简及相应的属性重要度,对具有偏好值属性的信息系统进行更加高效的处理,通过多粒度的表示和关系的研究,加速了知识约简过程。选取了多组UCI数据进行实验,结果表明所提出的基于优势关系的序贯三支决策方法能够在保证约简质量的基础上明显降低时间耗费。

关键词: 粗糙集, 决策理论粗糙集, 序贯三支决策, 优势关系, 属性约简

Abstract: Sequential three-way decision is an effective way to solve problems under multiple levels granularity.Dominance-equivalence relation based rough set approach can be used to handle classification problems for conditional attri-butes with preference ordered,extract related information,approximate target concepts and finally form the decision-making knowledge.The traditional dominance relation-based rough sets model is very time consuming for knowledge reduction and extraction,however,most of current sequential three-way decision models are limited to information systems of symbolic attributes,which can not process continuous and ordinal values effectively,and will cause a certain degree loss of information.Therefore,this paper applied the idea of sequential three-way decisions to the dominance relation-based rough sets models,defined a new attribute reduction method based on sequential three-way decisions and the corresponding attribute importance measure,and thenaccelerated the processing of information systems with ordinal attributes.Finally,the efficiency of knowledge reduction is improved through multiple granularity representations and relationships.Several UCI data sets are selected for experiments.The results show that the proposed sequential three-decision method based on dominance relations can reduce the time consumption noticeably and guarantee the quality of the attribute reduction.

Key words: Attribute reduction, Decision theory rough set, Dominance relation, Rough set, Sequential three-way decisions

中图分类号: 

  • TP181
[1]GRECO S,MATARAZZO B,SLOWINSKI R.Rough approximation by dominance relations[J].International Journal of Intelligent Systems,2002,17(2):153-171.
[2]CHEN J,WANG G Y,HU J.Positive domain reduction based on dominance relation in inconsistent system[J].ComputerScien-ce,2008,35(3):216-218.(in Chinese)
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):216-218.
[3]LI Y,SUN N X,ZHAO J,et al.Reductions based on dominance-equivalence relations and rule extraction methods [J].Computer Science,2011,38(11):220-224.(in Chinese)
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.
[4]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.
[5]AWANG M I,ROSE A N M,AWEANG M K,et al.Multiple criteria preference relation by dominance relations in soft set theory[M].Berlin:Springer International Publishing,2016:475-484.
[6]YAO Y Y.Decision-theoretic rough set models[C]∥International Conference on Rough Sets and Knowledge Technology.Sprin-ger,Berlin,Heidelberg,2007:1-12.
[7]YAO Y Y,WONG S K M.A decision theoretic framework for approximating concepts[J].International Journal of Man-Machine Studies,1992,37(6):793-809.
[8]YAO Y Y,ZHAO Y.Attribute reduction in decision-theoretic rough set models[J].Information Sciences,2008,178(17):3356-3373.
[9]YAO Y Y.An outline of a theory of three-way decisions[C]∥ International Conference on Rough Sets and Current Trends in Computing.Springer,Berlin:Heidelberg,2012:1-17.
[10]贾修一,商林,周献中,等.三支决策理论与应用[M].南京:南京大学出版社,2012.
[11]YAO Y.Rough sets and three-way decisions[M].Berlin: Springer International Publishing,2015.
[12]LIU D,YAO Y Y,LI T R.Three-way investment decisions with decision-theoretic rough sets[J].International Journal of Computational Intelligence Systems,2011,4(1):66-74.
[13]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.
[14]YU H,LIU Z,WANG G.An automatic method to determine the number of clusters using decision-theoretic rough set[M].Amsterdam:Elsevier Science Inc.2014.
[15]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(C):241-251.
[16]JIA X,SHANG L,ZHOU B,et al.Generalized attribute reduct in rough set theory[J].Knowledge-Based Systems,2016,91(C):204-218.
[17]MENG Z,SHI Z.On quick attribute reduction in decision-theo- retic rough set models[J].Information Sciences,2016,330:226-244.
[18]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.
[19]LI J,MEI C,XU W,et al.Concept learning via granular computing:a cognitive viewpoint[J].Information Sciences,2015,298(1):447-467.
[20]PAWLAK Z.Rough sets:theoretical aspects of reasoning about data[M].Boston:Kluwer Academic Publishers,1991.
[21]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.
[22]XU W H,ZHANG X X,ZHANG W X.Upper approximation reduction in inconsistent target information system based on dominance relations [J].Computer Engineering,2009,35(18):191-193.(in Chinese)
徐伟华,张晓燕,张文修.优势关系下不协调目标信息系统的上近似约简[J].计算机工程,2009,35(18):191-193.
[23]BACHE K,LICHMA M.UCI Machine Learning Repository [OL].http://archive.ics.uci.edu/ml.
[1] 程富豪, 徐泰华, 陈建军, 宋晶晶, 杨习贝.
基于顶点粒k步搜索和粗糙集的强连通分量挖掘算法
Strongly Connected Components Mining Algorithm Based on k-step Search of Vertex Granule and Rough Set Theory
计算机科学, 2022, 49(8): 97-107. https://doi.org/10.11896/jsjkx.210700202
[2] 许思雨, 秦克云.
基于剩余格的模糊粗糙集的拓扑性质
Topological Properties of Fuzzy Rough Sets Based on Residuated Lattices
计算机科学, 2022, 49(6A): 140-143. https://doi.org/10.11896/jsjkx.210200123
[3] 方连花, 林玉梅, 吴伟志.
随机多尺度序决策系统的最优尺度选择
Optimal Scale Selection in Random Multi-scale Ordered Decision Systems
计算机科学, 2022, 49(6): 172-179. https://doi.org/10.11896/jsjkx.220200067
[4] 陈于思, 艾志华, 张清华.
基于三角不等式判定和局部策略的高效邻域覆盖模型
Efficient Neighborhood Covering Model Based on Triangle Inequality Checkand Local Strategy
计算机科学, 2022, 49(5): 152-158. https://doi.org/10.11896/jsjkx.210300302
[5] 孙林, 黄苗苗, 徐久成.
基于邻域粗糙集和Relief的弱标记特征选择方法
Weak Label Feature Selection Method Based on Neighborhood Rough Sets and Relief
计算机科学, 2022, 49(4): 152-160. https://doi.org/10.11896/jsjkx.210300094
[6] 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
[7] 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
[8] 薛占熬, 侯昊东, 孙冰心, 姚守倩.
带标记的不完备双论域模糊概率粗糙集中近似集动态更新方法
Label-based Approach for Dynamic Updating Approximations in Incomplete Fuzzy Probabilistic Rough Sets over Two Universes
计算机科学, 2022, 49(3): 255-262. https://doi.org/10.11896/jsjkx.201200042
[9] 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
[10] 薛占熬, 孙冰心, 侯昊东, 荆萌萌.
基于多粒度粗糙直觉犹豫模糊集的最优粒度选择方法
Optimal Granulation Selection Method Based on Multi-granulation Rough Intuitionistic Hesitant Fuzzy Sets
计算机科学, 2021, 48(10): 98-106. https://doi.org/10.11896/jsjkx.200800074
[11] 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
[12] 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
[13] 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
[14] 陈玉金, 徐吉辉, 史佳辉, 刘宇.
基于直觉犹豫模糊集的三支决策模型及其应用
Three-way Decision Models Based on Intuitionistic Hesitant Fuzzy Sets and Its Applications
计算机科学, 2020, 47(8): 144-150. https://doi.org/10.11896/jsjkx.190800041
[15] 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
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!