计算机科学 ›› 2018, Vol. 45 ›› Issue (6): 247-250.doi: 10.11896/j.issn.1002-137X.2018.06.044

• 人工智能 • 上一篇    下一篇

决策系统基于不可区分关系及区分关系的约简

秦克云, 敬思惠   

  1. 西南交通大学数学学院 成都610031
  • 收稿日期:2017-03-01 出版日期:2018-06-15 发布日期:2018-07-24
  • 作者简介:秦克云(1962-),男,教授,博士生导师,CCF高级会员,主要研究方向为粗糙集理论、粒计算、多值逻辑,E-mail:keyunqin@263.net(通信作者);敬思惠(1993-),女,硕士生,主要研究方向为粗糙集理论
  • 基金资助:
    本文受国家自然科学基金(61473239,61372187),西华大学省部级学科平台开放课题(szjj2014-052)资助

Attribute Reduction of Decision Systems Based on Indiscernibility Relation and Discernibility Relation

QIN Ke-yun, JING Si-hui   

  1. College of Mathematics,Southwest Jiaotong University,Chengdu 610031,China
  • Received:2017-03-01 Online:2018-06-15 Published:2018-07-24

摘要: 信息系统中的知识约简和知识发现是粗糙集理论的重要研究方向。针对决策系统中的不可区分关系及区分关系,给出相应的协调集判定定理,进而借助区分矩阵及区分函数给出属性约简方法,并借助实例将其与已有的相关研究工作进行了对比分析。

关键词: 不可区分关系, 粗糙集, 区分关系, 属性约简

Abstract: Knowledge reduction and knowledge discovery in the information systems are important topics of rough set theory.Based on the indiscernibility relation and discernibility relation of decision systems,the judgement theorems for relative indiscernibility consistent set and relative discernibility consistent set were provided.This paper gave the attri-bute reduction method by discernibility matrices and discernibility functions,and analyzed it and relevant research work by means of examples.

Key words: Attribute reduction, Discernibility relation, Indiscernibility relation, Rough set

中图分类号: 

  • TP181
[1]PAWLAK Z.Rough set [J].International Journal of Computer and Information Science,1982,11(5):341-356.
[2]PAWLAK Z,SOWINSKI R.Rough set approach to multi-attribute decision analysis[J].European Journal of Operational Research,1993,72(3):443-459.
[3]KRYSZKIEWICZ M.Comparative studies of alternative type of knowledge reduction in inconsistent systems[J].International Journal of Intelligent Systems,2001,16(1):105-120.
[4]ZHANG W X,MI J S,WU W Z.Knowledge reduction in inconsistent information systems[J].Chinese Journal of Computers,2003,26(1):12-18.(in Chinese)
张文修,米据生,吴伟志.不协调目标信息系统的知识约简[J].计算机学报,2003,26(1):12-18.
[5]ZHANG W X,MI J S,WU W Z.Approaches to knowledge reductions in inconsistent systems[J].International Journal of Intelligent Systems,2003,18(9):989-1000.
[6]WANG G Y,YU H,YANG D C.Decision table reduction based on conditional information entropy[J].Chinese Journal of Computers,2002,25(7):759-766.(in Chinese)
王国胤,于洪,杨大春.基于条件信息熵的决策表约简[J].计算机学报,2002,25(7):759-766.
[7]YE D Y,CHEN Z J.A new type of attribute reduction for inconsistent decision tables and its computation[J].International Journal of Uncertainty,Fuzziness and Knowledge-Based Systems,2010,18(2):209-222.
[8]MIAO D Q,ZHAO Y,YAO Y Y,et al.Relative reducts in consistent and inconsistent decision tables of the Pawlak rough set model[J].Information Sciences,2009,179(24):4140-4150.
[9]MENG Z Q,SHI Z Z.Extended rough set-based attribute reduction in inconsistent incomplete decision systems[J].Information Sciences,2012,204(20):44-66.
[10]YAO Y Y,ZHAO Y.Attribute reduction in decision-theoretic rough set models[J].Information Sciences,2008,178(17):3356-3373.
[11]YAO Y Y.The superiority of three-way decision in probabilistic rough set models[J].Information Sciences,2011,181(6):1080-1096.
[12]SKOWRON A,RAUSZER C.The discernibility matrices and
functions in information systems[M].Dordrecht:Kluwer Academic Publishers,1992:331-362.
[13]WANG J,WANG R,MIAO D Q,et al.Data enriching based on rough set theory[J].Chinese Journal of Computers,1998,21(5):393-400.(in Chinese)
王珏,王任,苗夺谦,等.基于Rough Set理论的数据浓缩[J].计算机学报,1998,21(5):393-400.
[14]MIAO D Q,HU G R.A heuristic algorithm for reduction of knowledge[J].Journal of Computer Research and Development,1999,36(6):681-684.(in Chinese)
苗夺谦,胡桂荣.知识约简的一种启发式算法[J].计算机研究与发展,1999,36(6):681-684.
[15]JIA P,DAI J H,PAN Y H,et al.Novel algorithm for attribute reduction based on mutual-information gain ratio[J].Journal of Zhejiang University,2006,40(6):1041-1045.(in Chinese)
贾平,代建华,潘云鹤,等.一种基于互信息增益率的新属性约简算法[J].浙江大学学报,2006,40(6):1041-1045.
[16]LI M,SHANG C X,FENG S Z,et al.Quick attribute reduction in inconsistent decision tables[J].Information Sciences,2014,254(1):155-180.
[17]LI B,CHWO T W S,TANG P.Analyzing rough set based attribute reductions by extension rule[J].Neurocomputing,2014,123(123):185-196.
[18]ZHAO Y,YAO Y Y,LUO F.Data analysis based on discernibility and indiscernibility[J].Information Sciences,2007,177(22):4959-4976.
[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!