计算机科学 ›› 2023, Vol. 50 ›› Issue (5): 137-145.doi: 10.11896/jsjkx.220500268
杨洁1,2, 匡俊成1, 王国胤1, 刘群1
YANG Jie1,2, KUANG Juncheng1, WANG Guoyin1, LIU Qun1
摘要: 多粒度邻域粗糙集是邻域粗糙集理论的一种新型数据处理模式,其目标概念分别由乐观和悲观的上、下近似边界描述。但当前的多粒度邻域粗糙集既缺乏利用已有的信息粒近似描述目标概念的方法,又无法处理目标概念为模糊的情形。而张清华教授提出的粗糙集近似理论提供了一种利用已有信息粒近似描述知识的方法,为构建多粒度邻域粗糙模糊集的近似精确集提供了新思路。文中首先针对模糊目标概念,将粗糙集近似理论应用到邻域粗糙集领域,提出了代价敏感的邻域粗糙模糊集的近似表示模型;然后进一步从多粒度视角,构建出一种代价敏感的邻域粗糙模糊集的多粒度近似表示模型,并分析了其相关性质;最后,通过实验仿真,验证了当多粒度代价敏感近似及其上、下近似方法分别去近似刻画模糊目标概念时,多粒度代价敏感近似方法产生的误分类代价最小。
中图分类号:
[1]WANG G Y,YANG J,XU J.Granular computing:from granularity optimization to multi-granularity joint problem solving[J].Granular Computing,2017,2(3):105-120. [2]ZADEH L A.Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J].Fuzzy Set and Systems,1997,90(2):111-127. [3]SONG M L,WANG Y B.A study of granular computing in the agenda of growth of artificial neural networks[J].Granular Computing,2016,1:247-257. [4]YAO Y Y.Three-way decision and granular computing[J].International Journal of Approximate Reasoning,2018,103:107-123. [5]QI J J,WEI,WAN Q.Multi-level granularity in formal concept analysis[J].Granular Computing,2019,4:351-362. [6]ZADEH L A.Fuzzy set[J].Information and Control,1965,8(3):338-353. [7]PAWLAK Z.Rough set[J].International Journal of Computer and Information Sciences,1982,11(5):341-356. [8]ZHANG L,ZHANG B.The quotient space theory of problemsolving[C]//International Workshop on Rough Set,Fuzzy Set,Data Mining,and Granular Soft Computing.2003:11-15. [9]LI D Y,MENG H J,SHI X M.Membership clouds and membership cloud generators[J].Journal of Computer Research and Development,1995,32(6):15-20. [10]LI J H,WANG F,WU W Z,et al.Review of Multi-granularity Data Analysis Methods Based on Granular Computing[J].Journal of Data Acquisition and Processing,2021,36(3):418-435. [11]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,2009,40(2):420-431. [12]LIN G P,QIAN Y H,LI J J.Nmgrs:Neighborhood-based multigranulation rough set[J].International Journal of Approximate Reasoning,2012,53(7):1080-1093. [13]SUN L,WANG L,DING W,et al.Feature Selection UsingFuzzy Neighborhood Entropy-Based Uncertainty Measures for Fuzzy Neighborhood Multigranulation Rough Set[J].IEEE Transactions on Fuzzy Systems,2021,29(1):19-33. [14]GUO Y,TSANG E C C,HU M.Local Weighted Generalized Multigranulation Neighborhood Rough Set[C]//2021 International Conference on Wavelet Analysis and Pattern Recognition(ICWAPR).2021:1-7. [15]YANG J,HUANG M Y,CHEN J.Neighborhood-based multi-granulation rough fuzzy set and their uncertainty measure[J].Journal of Chongqing University of Posts and Telecommunications(Natural Science Edition),2020,32(5):898-908. [16]ZHANG Q H,WANG G Y,YU X.Approximation set of rough set[J].Journal of Software,2012,23(7):1745-1759. [17]ZHANG Q H,WANG J,WANG G Y.The approximate representation of rough-fuzzy set[J].Chinese Journal of Computers.Jisuanji Xuebao,2015,38(7):1484-1496. [18]ZHANG Q H,WANG J,WANG G Y.The approximation set of a vague set in rough approximation space[J].Information Sciences,2015(300):1-19. [19]ZHANG Q H,YANG J J,YAO L Y.Attribute reduction based on rough approximation set in algebra and information views[J].IEEE Access,2016(4):5399-5407. [20]YAO L Y,ZHANG Q H,HU S P,et al.Rough entropy forimage segmentation based on approximation set and particle swarm optimization[J].Journal of Frontiers of Computer Science and Technology,2016,10(5):699-708. [21]YAO Y Y.Tri-level thinking:models of three-way decision[J].International Journal of Machine Learning and Cybernetics,2020,11(5):947-959. [22]HU Q H,YU D,LIU J H,et al.Neighborhood rough set based heterogeneous feature subset selection[J].Information Sciences,2008,178(18):3577-3594. [23]DUBOIS D,PRADE H.Rough fuzzy set and fuzzy rough set[J].International Journal of General System,1990,17(2/3):191-209. |
|