Computer Science ›› 2022, Vol. 49 ›› Issue (3): 255-262.doi: 10.11896/jsjkx.201200042

• Artificial Intelligence • Previous Articles     Next Articles

Label-based Approach for Dynamic Updating Approximations in Incomplete Fuzzy Probabilistic Rough Sets over Two Universes

XUE Zhan-ao, HOU Hao-dong, SUN Bing-xin, YAO Shou-qian   

  1. College of Computer and Information Engineering,Henan Normal University,Xinxiang,Henan 453007,China
    Key Laboratory of Artificial Intelligence and Personalized Learning in Education of Henan Province,Xinxiang,Henan 453007,China
  • Received:2020-12-03 Revised:2021-05-13 Online:2022-03-15 Published:2022-03-15
  • About author:XUE Zhan-ao,born in 1963,Ph.D,professor,is a senior member of Chinese Artificial Intelligence Association.His main research interests include basic theory of artificial intelligence,rough sets theory,fuzzy sets,and three-way decision theory.
  • Supported by:
    National Natural Science Foundation of China(62076089,61772176) and Scientific and Technological Project of Henan Province of China(182102210078,182102210362).

Abstract: When the missing values are obtained in incomplete fuzzy probabilistic rough sets over two universes,the time efficiency of the traditional static algorithm for updating approximations in incomplete fuzzy probabilistic rough sets over two universes is too low.To solve this problem,a label-based approach for dynamic updating approximations in incomplete fuzzy probabilistic rough sets over two universes isstudied.Firstly,some definitions of incomplete fuzzy probabilistic rough over two universes are given,then based on the matrix method,a label-based model of incomplete fuzzy probabilistic rough sets over two universes is proposed,and the related theorems are proved.After that,a label-based method for calculating approximations in incomplete fuzzy probabilistic rough sets over two universes is proposed and analyzed.Then,when the missing values are obtained in incomplete fuzzy probabilistic rough sets over two universes,the theorem for dynamic updating its approximations is proved,and a label-based algorithm for dynamic updating approximations in incomplete fuzzy probabilistic rough sets over two universes is designed and analyzed.Finally,the simulation experiments are conducted on six datasets from UCI and three man-made datasets.The experimental results show that the proposed dynamic updating algorithm can improve the time efficiency of updating approximations.Then an example shows that the dynamic algorithm does not affect the correctness of the results when updating approximations,which proves the validity of the proposed dynamic updating algorithm.

Key words: Approximations, Dynamic updating, Incomplete information system over two universes, Label, Rough sets

CLC Number: 

  • TP181
[1]PAWLAK Z.Rough sets[J].International Journal Computer Information Science,1982,11(5):341-356.
[2]WANG C Z,HUANG Y,SHAO M,et al.Uncertainty measures for general fuzzy relations[J].Fuzzy Sets and Systems,2019,360:82-96.
[3]CHEN Y J,XU J H,SHI J H,et al.Three-way Decision Models Based on Intuitionistic Hesitant Fuzzy Sets and Its Applications[J].Computer Science,2020,47(8):144-150.
[4]SANG B B,ZHANG X Y.The Approach to Probabilistic Decision-Theoretic Rough Set in Intuitionistic Fuzzy Information Systems[J].Intelligent Information Management,2020,12(1):1-26.
[5]NI P,ZHAO S Y,WANG X Z,et al.Incremental feature selection based on fuzzy rough sets[J].Information Sciences,2020,536:185-204.
[6]JUAN L U.Type-2 fuzzy multigranulation rough sets[J].International Journal of Approximate Reasoning,2020,124:173-193.
[7]MANDAL P,RANADIVE A S.Multi-granulation fuzzy probabilistic rough sets and their corresponding three-way decisions over two universes[J].Iranian Journal of Fuzzy Systems,2019,16(5):61-76.
[8]LIN Y J,LI Y W,WANG C X,et al.Attribute reduction for multi-label learning with fuzzy rough set[J].Knowledge-Based Systems,2018,152:51-61.
[9]YAO Y Y,WONG S K M,WANG L S,et al.A non-numeric approach to uncertain reasoning[J].International Journal ofGe-neral Systems,1995,23(4):343-359.
[10]ZHANG H R,MIN F.Three-way recommender systems based on random forests[J].Knowledge Based Systems,2016,91:275-286.
[11]ZHANG H D,SHU L,LIAO S L,et al.Dual hesitant fuzzy rough set and its application[J].Soft Computing,2017,21(12):3287-3305.
[12]ZHANG C,LI D Y,ZHAI Y H,et al.Multigranulation roughset model in hesitant fuzzy information systems and its application in person-job fit[J].International Journal of Machine Learning and Cybernetics,2019,10(4):717-729.
[13]SUN B Z,MA W M.Fuzzy rough set model on two differentuniverses and its application[J].Applied Mathematical Modelling,2011,35(4):1798-1809.
[14]YANG H L,LIAO X W,WANG S Y,et al.Fuzzy probabilistic rough set model on two universes and its applications[J].International Journal of Approximate Reasoning,2013,54(9):1410-1420.
[15]QIAN Y H,LIANG J Y,DANG C Y.Incomplete multigranulation rough set[J].IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans,2010,40(2):420-431.
[16]SAEDUDIN R R,KASIM S,MAHDIN H,et al.A Relative To-lerance Relation of Rough Set in Incomplete Information[J].Sains Malaysiana,2020,48(12):2831-2839.
[17]XUE Z A,ZHANG M,ZHAO L P,et al.Variable Three-way Decision Model of Multi-granulation Decision Rough Sets Under Set-pair Dominance Relation[J].Computer Science,2021,48(1):157-166.
[18]LANG G M,CAI M J,FUJITA H,et al.Related families-based attribute reduction of dynamic covering decision information systems[J].Knowledge Based Systems,2018,162:161-173.
[19]WANG S,LI T R,LUO C,et al.Domain-wise approaches for updating approximations with multi-dimensional variation of ordered information systems[J].Information Sciences,2019(478):100-124.
[20]XIE X J,QIN X L.A novel incremental attribute reduction approach for dynamic incomplete decision systems[J].Internatio-nal Journal of Approximate Reasoning,2018,93:443-462.
[21]ZENG A P,LI T R,HU J,et al.Dynamical updating fuzzy rough approximations for hybrid data under the variation of attribute values[J].Information Sciences,2017,378:363-388.
[22]HUANG Q Q,LI T R,HUANG Y Y,et al.Dynamic dominance rough set approach for processing composite ordered data[J].Knowledge Based Systems,2020,187:104829.
[23]HU C X,LIU S X,HUANG X L,et al.Dynamic updating approximations in multigranulation rough sets while refining or coarsening attribute values[J].Knowledge Based Systems,2017,130:62-73.
[24]XU Y,XIAO P.Dynamic updating method of approximations in multigranulation rough sets based on tolerance relation[J].Journal of Computer Applications,2019,39(5):1247-1251.
[25]YU J H,XU W H.Incremental knowledge discovering in interval-valued decision information system with the dynamic data[J].International Journal of Machine Learning and Cybernetics,2017,8(3):849-864.
[26]LUO C,LI T R,YI Z,et al.Matrix approach to decision-theore-tic rough sets for evolving data[J].Knowledge Based Systems,2016,99:123-134.
[27]CHEN H M,LI T R,RUAN D,et al.A Rough-Set-Based Incremental Approach for Updating Approximations under Dynamic Maintenance Environments [J].IEEE Transactions on Know-ledge and Data Engineering,2013,25(2):274-284.
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