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
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