Computer Science ›› 2020, Vol. 47 ›› Issue (3): 61-66.doi: 10.11896/jsjkx.190500174

• Database & Big Data & Data Science • Previous Articles     Next Articles

Neighborhood Knowledge Distance Measure Model Based on Boundary Regions

YANG Jie 1,2,WANG Guo-yin1,LI Shuai1   

  1. (Chongqing Key Laboratory of Computational Intelligence, Chongqing University of Post and Telecommunications, Chongqing 400065, China)1;
    (School of Physics and Electronic Science, Zunyi Normal University, Zunyi, Guizhou 563002, China)2
  • Received:2019-05-30 Online:2020-03-15 Published:2020-03-30
  • About author:YANG Jie,born in 1987,Ph.D,associate professor.His research interests include data mining,machine learning,three-way decision and rough set. WANG Guo-yin,born in 1970,Ph.D,professor.His research interests include data mining,machine learning,granular computing and rough set.
  • Supported by:
    This work was supported by the National Science Foundation of China (61572091, 61472056), High level Innovative Talents Project of Guizhou Province and Science ([2018]15) and technology talent growth project of Guizhou Province (KY (2018)318).

Abstract: Uncertainty measure of rough sets plays an important role in knowledge acquisition.In neighborhood rough sets,the current researches on uncertainty measure mainly focus on measuring the uncertainty of a single knowledge space and its monotonicity with the changing granularities.However,there are still some shortcomings.Firstly,the uncertainty of neighborhood rough set comes from elements belonging to target concept and elements not belonging to target concept in neighborhood granules,but current researches do not consider the two parts of each neighborhood information granule at the same time.Secondly,the difference between different knowledge spaces for describing the target concept is hard to reflect.Thirdly,the current knowledge distance measures are too fine,which contains granularity information and is inaccurate in some applications,i.e.heuristic search in attribute reduction.Therefore,based on the granularity measure of neighborhood information granules,this paper constructed the neighborhood entropy which is monotonic with the granularity being finer.In order to reflect the difference between different neighborhood information granule for describing the target concept,this paper proposed a neighborhood granule distance with approximate description ability,which is called relative neighborhood granule distance (RNGD).Then,several important properties were presented.The neighborhood knowledge distance based on boundary regions was established based on the RNGD,which can reflect the difference between different neighborhood knowledge spaces for describing the target concept.Finally,the validity of neighborhood knowledge distance based on decision regions was verified by experiments.

Key words: Knowledge distance, Neighborhood entropy, Neighborhood rough sets, Relative neighborhood granule distance, Uncertainty measure

CLC Number: 

  • TP311
[1]PEDRYCZ W,ALHMOUZ R,MORFEQ A,et al.The design of free structure granular mappings:the use of the principle of justifiable granularity [J].IEEE Transactions on Cybernetics,2013,43(6):2105-2113.
[2]PEDRYCZ W,SKOWRON A,KREINOVICH V.Handbook of granular computing [M].Wiley-Interscience,2008:719-740.
[3]YAO J T,VASILAKOS A V,PEDRYCZ W.Granular Computing:Perspectives and Challenges[J].IEEE Transactions on Cybernetics,2013,43(6):1977-1989.
[4]YAO Y Y.Perspectives of granular computing[C]∥IEEE International Conference on Granular Computing.IEEE,2005:85-90.
[5]WANG G Y,YANG J,XU J.Granular computing:from granularity optimization to multi-granularity joint problem solving [J].Granular Computing,2017,2(3):1-16.
[6]ZADEH L A.Fuzzy sets[J].Information and Control,1965, 8(3):338-353.
[7]PAWLAK Z.Rough sets[J].International Journal of Computer Information Sciences,1982,11(5):341-356.
[8]张钹,张铃.问题求解理论及应用[M].北京:清华大学出版社,1990.
[9]LI D Y,MENG H J.Membership and membership cloud genera- tor[J].Computer Research and Development,1995(6):15-20.
[10]LIN T Y.Neighborhood systems and relational databases[C]∥Proceedings of the 1988 ACM Sixteenth Annual Conference on Computer Science.ACM,1988:725.
[11]YAO Y Y.Relational interpretations of neighborhood operators and rough set approximation operators[J].Information Sciences,1998,111(1-4):239-259.
[12]YAO Y Y.Granular computing using neighborhood systems,advances in soft computing:engineering design and manufacturing[C]∥The 3rd On-line World Conference on Soft Computing.London:Springer,1999:539-553.
[13]HU Q H,YU D,XIE Z.Neighborhood classifiers[J].Expert Systems with Applications,2008,34(2):866-876.
[14]HU Q H,YU D,LIU J,et al.Neighborhood rough set based heterogeneous feature subset selection[J].Information Sciences,2008,178(18):3577-3594.
[15]LI W,HUANG Z,JIA X,et al.Neighborhood based decision-theoretic rough set models[J].International Journal of Approximate Reasoning,2016,69:1-17.
[16]YANG X,MING Z,DOU H,et al.Neighborhood systems-based rough sets in incomplete information system[J].Knowledge-Based Systems,2011,24(6):858-867.
[17]WANG Q,QIAN Y H,LIANG X Y,et al.Local neighborhood rough set[J].Knowledge-Based Systems,2018,153:53-64.
[18]YONG L,HUANG W,JIANG Y,et al.Quick attribute reduct algorithm for neighborhood rough set model[J].Information Sciences,2014,271(7):65-81.
[19]CHEN Y M,ZENG Z,LU J.Neighborhood rough set reduction with fish swarm algorithm[J].Soft Computing,2016,21(23):1-12.
[20]YING Y,PEDRYCZ W,MIAO D.Neighborhood rough sets based multi-label classification for automatic image annotation[J].International Journal of Approximate Reasoning,2013,54(9):1373-1387.
[21]KUMAR S U,INBARANI H H.Neighborhood rough set based ECG signal classification for diagnosis of cardiac diseases[J].Soft Computing,2016,21(16):4721-4733.
[22]XIE H,TAN K,WANG L,et al.Hyperspectral band selection based on a variable precision neighborhood rough set[J].Applied Optics,2016,55(3):462.
[23]ZHONG Y,ZHANG X,SHAN F.Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures[J].Expert Systems with Applications,2018,112:243-257.
[24]MENG J,JING Z,RUI L,et al.Gene selection using rough set based on neighborhood for the analysis of plant stress response[J].Applied Soft Computing,2014,25(C):51-63.
[25]CHEN Y M,WU K,CHEN X,et al.An entropy-based uncertainty measurement approach in neighborhood systems[J].Information Sciences,2014,279:239-250.
[26]CHEN Y M,XUE Y,MA Y,et al.Measures of uncertainty for neighborhood rough sets[J].Knowledge-Based Systems,2017,120:226-235.
[27]TANG Z H,CHEN Y M.Uncertainty measurement methods for neighborhood systems[J].Control and Decision,2014,29(4):691-695.
[28]ZHENG T,ZHU L.Uncertainty measures of Neighborhood System-based rough sets[J].Knowledge-Based Systems,2015,86:57-65.
[29]QIAN Y H,LIANG J Y,DANG C Y.Knowledge structure, knowledge granulation and knowledge distance in a knowledge base[J].International Journal of Approximate Reasoning,2009,50:174-188.
[30]LIANG J Y,RU L,QIAN Y H.Distance:A more comprehensible perspective for measures in rough set theory[J].Knowledge-Based Systems,2012,27(3):126-136.
[31]QIAN Y H,CHENG H,WANG J,et al.Grouping granular structures in human granulation intelligence[J].Information Sciences,2017,382:150-169.
[32]YANG X B,QIAN Y H,YANG J Y.On characterizing hierarchies of granulation structures via distances[J].Fundamenta Informaticae,2013,123(3):365-380.
[33]CHEN Y M,QIN N,LI W,et al.Granule structures,distances and measures in neighborhood systems[J].Knowledge-Based Systems,2018,165:268-281.
[34]YANG J,WANG G Y,ZHANG Q H.Knowledge Distance Measure in Multigranulation Spaces of Fuzzy Equivalence Relations[J].Information Sciences,2018,448-449:18-35.
[35]QIAN Y H,LI Y,LIANG J Y,et al.Fuzzy Granular Structure Distance[J].IEEE Transactions on Fuzzy Systems,2015,23(6):2245-2259.
[36]WANG G Y,ZHANG Q H.Uncertainty of Rough Sets in Different Knowledge Granularities[J].Chinese Journal ofCompu-ters,2008,31(9):1588-1598.
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[6] . [J]. Computer Science, 2007, 34(3): 151-154.
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