[1]

PAWLAK Z. Rough sets[J]. International Journal of Computer & Information Sciences, 1982, 11(5): 341-356.

[2]

苑红星, 卓雪雪, 竺德, 等. 基于矩阵的混合型邻域决策粗糙集增量式更新算法[J]. 控制与决策, 2022, 37(6): 1621-1631.

YUAN H X, ZHUO X X, ZHU D, et al. Rough set incremental update algorithm for mixed neighborhood decision based on matrix[J]. Control and Decision, 2022, 37(6): 1621-1631(in Chinese).

[3]

YAO Y Y. Relational interpretations of neighborhood operators and rough set approximation operators[J]. Information Sciences, 1998, 111(1-4): 239-259. doi: 10.1016/S0020-0255(98)10006-3

[4]

YUAN Z, CHEN H M, XIE P, et al. Attribute reduction methods in fuzzy rough set theory: An overview, comparative experiments, and new directions[J]. Applied Soft Computing, 2021, 107: 107353. doi: 10.1016/j.asoc.2021.107353

[5]

饶梦, 苗夺谦, 罗晟. 一种粗糙不确定的图像分割方法[J]. 计算机科学, 2020, 47(2): 72-75. doi: 10.11896/jsjkx.190500177

RAO M, MIAO D Q, LUO S. Rough uncertain image segmentation method[J]. Computer Science, 2020, 47(2): 72-75(in Chinese). doi: 10.11896/jsjkx.190500177

[6]

WANG G Q, LI T R, ZHANG P F, et al. Double-local rough sets for efficient data mining[J]. Information Sciences, 2021, 571: 475-498. doi: 10.1016/j.ins.2021.05.007

[7]

冀俊忠, 龙腾, 杨翠翠. 基于邻域决策粗糙集的脑功能连接生物标记物识别[J]. 控制与决策, 2023, 38(4): 1092-1100.

JI J Z, LONG T, YANG C C. Identifying brain functional connectivity biomarkers based on neighborhood decision rough set[J]. Control and Decision, 2023, 38(4): 1092-1100(in Chinese).

[8]

KONDO M. On topologies defined by neighbourhood operators of approximation spaces[J]. International Journal of Approximate Reasoning, 2021, 137: 137-145. doi: 10.1016/j.ijar.2021.07.010

[9]

HU Q H, PEDRYCZ W, YU D R, et al. Selecting discrete and continuous features based on neighborhood decision error minimization[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2010, 40(1): 137-150.

[10]

CHEN H M, LI T R, FAN X, et al. Feature selection for imbalanced data based on neighborhood rough sets[J]. Information Sciences, 2019, 483: 1-20.

[11]

CHEN H M, LI T R, CAI Y, et al. Parallel attribute reduction in dominance-based neighborhood rough set[J]. Information Sciences, 2016, 373: 351-368. doi: 10.1016/j.ins.2016.09.012

[12]

邓志轩, 郑忠龙, 邓大勇. F-邻域粗糙集及其约简[J]. 自动化学报, 2021, 47(3): 695-705.

DENG Z X, ZHENG Z L, DENG D Y. F-neighborhood rough sets and its reduction[J]. Acta Automatica Sinica, 2021, 47(3): 695-705(in Chinese).

[13]

WANG C Z, SHI Y P, FAN X D, et al. Attribute reduction based on k-nearest neighborhood rough sets[J]. International Journal of Approximate Reasoning, 2019, 106: 18-31. doi: 10.1016/j.ijar.2018.12.013

[14]

GUO Y T, TSANG E C C, XU W H, et al. Adaptive weighted generalized multi-granulation interval-valued decision-theoretic rough sets[J]. Knowledge-Based Systems, 2020, 187: 104804. doi: 10.1016/j.knosys.2019.06.012

[15]

TSANG E C C, HU Q H, CHEN D G. Feature and instance reduction for PNN classifiers based on fuzzy rough sets[J]. International Journal of Machine Learning and Cybernetics, 2016, 7(1): 1-11. doi: 10.1007/s13042-014-0232-6

[16]

VLUYMANS S, MAC PARTHALÁIN N, CORNELIS C, et al. Weight selection strategies for ordered weighted average based fuzzy rough sets[J]. Information Sciences, 2019, 501: 155-171. doi: 10.1016/j.ins.2019.05.085

[17]

KUMAR R, SINGH S, BILGA P S, et al. Revealing the benefits of entropy weights method for multi-objective optimization in machining operations: Acritical review[J]. Journal of Materials Research and Technology, 2021, 10: 1471-1492. doi: 10.1016/j.jmrt.2020.12.114

[18]

YOU C S, YANG S Y. A simple and effective multi-focus image fusion method based on local standard deviations enhanced by the guided filter[J]. Displays, 2022, 72: 102146. doi: 10.1016/j.displa.2021.102146

[19]

徐宇恒, 程嗣怡, 庞梦洋. 基于CRITIC-TOPSIS的动态辐射源威胁评估[J]. 北京航空航天大学学报, 2020, 46(11): 2168-2175.

XU Y H, CHENG S Y, PANG M Y. Dynamic radiator threat assessment based on CRITIC-TOPSIS[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(11): 2168-2175(in Chinese).

[20]

吴希. 三种权重赋权法的比较分析[J]. 中国集体经济, 2016(34): 73-74. doi: 10.3969/j.issn.1008-1283.2016.34.039

WU X. Comparative analysis of three weight empowerment methods[J]. China’s Collective Economy, 2016(34): 73-74(in Chinese). doi: 10.3969/j.issn.1008-1283.2016.34.039

[21]

ZIELOSKO B, STAŃCZYK U. Condition attributes, properties of decision rules, and discretisation: Analysis of relations and dependencies[J]. Procedia Computer Science, 2021, 192: 3922-3931. doi: 10.1016/j.procs.2021.09.167

[22]

BHADRA D, DHAR N R, SALAM M A. Sensitivity analysis of the integrated AHP-TOPSIS and CRITIC-TOPSIS method for selection of the natural fiber[J]. Materials Today: Proceedings, 2022, 56: 2618-2629. doi: 10.1016/j.matpr.2021.09.178

[23]

李冬. 基于邻域粗糙集的属性约简算法研究及应用[D]. 成都:成都信息工程大学, 2020.

LI D. Research and application of attribute reduction algorithm based on neighborhood rough set[D]. Chengdu: Chengdu University of Information Technology, 2020(in Chinese).

[24]

吴尚智, 王旭文, 王志宁, 等. 利用粗糙集和支持向量机的银行借贷风险预测模型[J]. 成都理工大学学报(自然科学版), 2022, 49(2): 249-256.

WU S Z, WANG X W, WANG Z N, et al. Prediction model of bank lending risk using rough set and support vector machine[J]. Journal of Chengdu University of Technology (Science & Technology Edition), 2022, 49(2): 249-256(in Chinese).

[25]

XIONG L, YAO Y. Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm[J]. Building and Environment, 2021, 202: 108026. doi: 10.1016/j.buildenv.2021.108026

[26]

AKRAM-ALI-HAMMOURI Z, FERNÁNDEZ-DELGADO M, ALBTOUSH A, et al. Ideal kernel tuning: Fast and scalable selection of the radial basis kernel spread for support vector classification[J]. Neurocomputing, 2022, 489: 1-8. doi: 10.1016/j.neucom.2022.03.034

[27]

HUANG Y Y, GUO K J, YI X W, et al. Matrix representation of the conditional entropy for incremental feature selection on multi-source data[J]. Information Sciences, 2022, 591: 263-286. doi: 10.1016/j.ins.2022.01.037