JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (1): 51-61.doi: 10.6040/j.issn.1671-9352.1.2019.167

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Incremental method for approximating sets of multi-granularity rough sets

ZHANG Hai-yang1, MA Zhou-ming1,2*, YU Pei-qiu1, LIN Meng-lei1, LI Jin-jin1   

  1. 1. School of Mathematics and Statistics, Minnan Normal University, Zhangzhou 363000, Fujian, China;
    2. Digital Fujian Meteorological Big Data Research Institute, Zhangzhou 363000, Fujian, China
  • Published:2020-01-10

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Abstract: Dynamic updating the upper and lower approximations in multi-granulation rough sets based on column matrix mainly use the relative correct classification rate to consider the varying attribute values and varying universe simultaneously. First, we discuss some properties of upper and lower approximation operators of multi-granulation rough sets while the universe decreasing and adding attributes, and give an updating method for approximations based on column matrix. Second, we discuss some properties of approximation operators while the universe decreasing and adding attributes, and give an updating method based on column matrix. The methods we proposed effectively shrink the searching region when updating the approximations of the multi-granulation rough sets.

Key words: incremental computing, updating approximation, multi-granulation rough set, column matrix

CLC Number: 

  • TP18
[1] PAWLAK Z. Rough sets[J]. International Journal of Computer & Information Sciences, 1982, 11(5):341-356.
[2] CHEN W. Evidence of electroconformational changes in membrane proteins: field-induced reductions in intra membrane nonlinear charge movement currents[J]. Bioelectrochemistry, 2004, 63(1):333-335.
[3] LEUNG Y, FISCHER M M, WU W Z, et al. A rough set approach for the discovery of classification rules in interval-valued information systems[J]. International Journal of Approximate Reasoning, 2008, 47(2):233-246.
[4] LIN Y, HU X, WU X. Quality of information-based source assessment and selection[J]. Neurocomputing, 2014, 133:95-102.
[5] QIAN Y, ZHANG H, LI F, et al. Set-based granular computing: a lattice model[J]. International Journal of Approximate Reasoning, 2014, 55(3):834-852.
[6] QIAN Y, ZHANG H, SANG Y, et al. Multigranulation decision-theoretic rough sets[J]. International Journal of Approximate Reasoning, 2014, 55(1):225-237.
[7] WU W Z, LEUNG Y, SHAO M W. Generalized fuzzy rough approximation operators determined by fuzzy implicators[J]. International Journal of Approximate Reasoning, 2013, 54(9):1388-1409.
[8] YAO Y. Probabilistic rough set approximations[J]. International Journal of Approximate Reasoning, 2008, 49(2):255-271.
[9] MIN F, HU Q, ZHU W. Feature selection with test cost constraint[J]. International Journal of Approximate Reasoning, 2014, 55(1):167-179.
[10] HU Q, SHUANG A, XIAO Y, et al. Robust fuzzy rough classifiers[J]. Fuzzy Sets & Systems, 2011, 183(1):26-43.
[11] LIU D, LI T, LIANG D. Incorporating logistic regression to decision-theoretic rough sets for classifications[J]. International Journal of Approximate Reasoning, 2014, 55(1):197-210.
[12] LUO C, LI T, CHEN H. Dynamic maintenance of approximations in set-valued ordered decision systems under the attribute generalization[J]. Information Sciences, 2014, 257:210-228.
[13] SWINIARSKI R W, SKOWRON A. Rough set methods in feature selection and recognition[J]. Pattern Recognition Letters, 2003, 24(6):833-849.
[14] ZHU P, HU Q. Rule extraction from support vector machines based on consistent region covering reduction[J]. Knowledge-Based Systems, 2013, 42(2):1-8.
[15] PAWLAK Z, SKOWRON A. Rough sets: some extensions[J]. Information Sciences, 2007, 177(1):28-40.
[16] 李进金, 谭安辉, 石素玮. 一类覆盖粗糙直觉模糊集模型的模糊粗糙度和粗糙熵[J]. 山东大学学报(理学版), 2014, 49(8):86-91. LI Jinjin, TAN Anhui, SHI Suwei. Fuzzy roughness and rough entropy of a class of models covering intuitionistic fuzzy sets[J]. Journal of Shandong University(Natural Science), 2014, 49(8):86-91.
[17] KATZBERG J D, ZIARKO W. Variable precision rough sets with asymmetric bounds[M] // Rough Sets, Fuzzy Sets and Knowledge Discovery. London: Springer, 1994: 167-177.
[18] YAO Y. Three-way decisions with probabilistic rough sets[J]. Information Sciences, 2010, 180(3):341-353.
[19] WU W Z, MI J S, ZHANG W X. Generalized fuzzy rough sets[J]. Information Sciences, 2003, 151(3):263-282.
[20] ZHAO S, TSANG E C C, CHEN D. The model of fuzzy variable recision rough sets[J]. IEEE Transanctions on Fuzzy System, 2009, 17(2):451-467.
[21] QIAN Y, LIANG J, YAO Y, et al. MGRS: a multi-granulation rough set ☆[J]. Information Sciences An International Journal, 2010, 180(6):949-970.
[22] QIAN Y, LIANG J, PEDRYCZ W, et al. Positive approximation: an accelerator for attribute reduction in rough set theory[J]. Artificial Intelligence, 2010, 174(9/10):597-618.
[23] LIN G, QIAN Y, LI J. NMGRS: neighborhood-based multigranulation rough sets[J]. International Journal of Approximate Reasoning, 2012, 53(7):1080-1093.
[24] FENG T, MI J S. Variable precision multigranulation decision-theoretic fuzzy rough sets[J]. Knowledge-Based Systems, 2016, 91:93-101.
[25] 胡谦, 米据生, 李磊军. 多粒度模糊粗糙近似算子的信任结构与属性约简[J]. 山东大学学报(理学版), 2017,52(7):30-36. HU Qian, MI Jusheng, LI Leijun. Trust structure and attribute reduction of multi-granularity fuzzy rough approximation operator[J]. Journal of Shandong University(Natural Science), 2017, 52(7):30-36.
[26] HU C, LIU S, LIU G. Matrix-based approaches for dynamic updating approximations in multigranulation rough sets[J]. Knowledge-Based Systems, 2017, 122:51-63.
[27] JU H, YANG X, SONG X, et al. Dynamic updating multigranulation fuzzy rough set: approximations and reducts[J]. International Journal of Machine Learning and Cybernetics, 2014, 5(6):981-990.
[28] 陈红梅. 粗糙集中基于粒计算的动态知识更新方法研究[D]. 成都:西南交通大学, 2013. CHEN Hongmei. Research on dynamic knowledge updating method based on grain computation in rough set [D]. Chengdu: Southwest Jiaotong University, 2013.
[29] 刘财辉, 苗夺谦. 基于矩阵的粗糙集上、下近似求解算法[J]. 计算机应用研究, 2011, 28(5):1628-1630. LIU Caihui, MIAO Duoqian. Rough set approximation algorithm based on matrix[J]. Computer Application Research, 2011, 28(5):1628-1630.
[30] YANG X, QI Y, YU H, et al. Updating multigranulation rough approximations with increasing of granular structures[J]. Knowledge-Based Systems, 2014, 64:59-69.
[31] HU C, LIU S, HUANG X. Dynamic updating approximations in multigranulation rough sets while refining or coarsening attribute values[J]. Knowledge-Based Systems, 2017, 130(15):62-73.
[32] 胡成祥, 赵国柱. 优势关系多粒度粗糙集中近似集动态更新方法[J]. 中国科学技术大学学报, 2017(1):43-50. HU Chengxiang, ZHAO Guozhu. Multi-granularity rough set approximation set dynamic update method[J]. Journal of University of Science and Technology of China, 2017(1):40-47.
[33] ZHANG J, LI T, RUAN D, et al. Neighborhood rough sets for dynamic data mining[J]. International Journal of Intelligent Systems, 2012, 27(4):317-342.
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