计算机科学 ›› 2020, Vol. 47 ›› Issue (3): 61-66.doi: 10.11896/jsjkx.190500174
杨洁1,2,王国胤1,李帅1
YANG Jie 1,2,WANG Guo-yin1,LI Shuai1
摘要: 粗糙集的不确定性度量在知识获取中扮演着非常重要的角色。在邻域粗糙集理论中,当前不确定性度量方面的研究工作主要专注于度量单个知识空间的不确定性及其随粒度变化的单调性规律,其仍存在以下缺点:1)邻域粗糙集不确定性来自于邻域粒中属于目标概念的元素和不属于目标概念的元素,当前的方法没有同时考虑每个邻域信息粒的这两部分;2)不能反映不同知识空间对目标概念刻画能力的差异性;3)由于当前的知识距离包含了粒度划分的信息,已有方法在一些应用场合下不够准确,例如属性约简中的知识启发式搜索及其粒度选择。对此,文中首先构建了一种更加直观准确的邻域粗糙集的不确定性度量方法——邻域熵,并证明了不确定性度量随着粒度的细化具有单调性;为了反映不同邻域信息粒对目标概念刻画能力的差异性,提出了一种带近似描述能力的邻域粒距离,称为相对邻域粒距离,并介绍了它的相关性质;针对分层递阶的多粒度知识空间中的粒度选择问题,建立了基于边界域的邻域知识距离度量模型,该知识距离可以反映不同邻域知识空间对目标概念的刻画能力的差异性。
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[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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[14] | . 信息系统中的知识距离与知识粗糙熵 计算机科学, 2007, 34(3): 151-154. |
[15] | 牟克典. 时态Dempster—Shafer理论 计算机科学, 2003, 30(7): 4-6. |
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