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山东大学学报 (工学版) ›› 2024, Vol. 54 ›› Issue (6): 38-48.doi: 10.6040/j.issn.1672-3961.0.2023.198

• 机器学习与数据挖掘 • 上一篇    

一种改进的悲观多粒度粗糙集粒度约简算法

谢立1,叶军1,2*,赖鹏飞1,卢岚1,周浩岩1,李兆彬1   

  1. 1.南昌工程学院信息工程学院, 江西 南昌 330099;2.江西省水信息协同感知与智能处理重点实验室(南昌工程学院), 江西 南昌 330099
  • 发布日期:2024-12-26
  • 作者简介:谢立(1996— ),女,江西赣州人,硕士研究生,主要研究方向为粗糙集与粒计算理论. E-mail:787719479@qq.com. *通信作者简介:叶军(1968— ),男,江西万安人,教授,硕士生导师,硕士,主要研究方向为数据挖掘与知识发现、机器学习与人工智能、粗糙集与粒计算理论等. E-mail:2003992646@nit.edu.cn
  • 基金资助:
    江西省教育厅科技资助项目(GJJ211920);国家自然科学基金资助项目(61562061)

An improved granular reduction algorithm for pessimistic multi-granularity rough sets

XIE Li1, YE Jun1,2*, LAI Pengfei1, LU Lan1, ZHOU Haoyan1, LI Zhaobin1   

  1. 1. College of Information Engineering, Nanchang Institute of Engineering, Nanchang 330099, Jiangxi, China;
    2. Jiangxi Province Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing(Nanchang Institute of Engineering), Nanchang 330099, Jiangxi, China
  • Published:2024-12-26

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摘要： 针对以粒度内部重要度和粒度外部重要度不能有效度量非核粒度的重要度,无法获得有效启发信息使约简过早收敛的问题,提出以正域变化度量核粒度的重要度、以边界集变化度量非核粒度的重要度。新的度量方法不仅能度量核粒度的重要度,而且能度量非核粒度的重要度。以新的粒度重要度为依据,提出一种改进的悲观多粒度约简算法,与样本选择的启发式属性约简算法、信息熵的模糊ε-近似约简算法、粒度加速求解约简算法和邻域区分指数的特征选择算法相比,新算法可以减少迭代次数,能更有效地找到粒度约简子集。通过加州大学欧文分校(University of California Irvine, UCI)数据集进行试验,验证了算法的有效性和实用性。

关键词: 多粒度粗糙集, 粒度重要度, 粒度空间, 粒度约简, 核粒度

中图分类号: 

  • TP18
[1] SONG S M, REN X J, HE J, et al. An optimal hierarchical approach for oral cancer diagnosis using rough set theory and an amended version of the competitive search algorithm[J]. Diagnostics, 2023, 13(14):2454.
[2] PEDRYCZ W. Granular computing for data analytics: a manifesto of human-centric computing[J]. IEEE/CAA Journal of Automatica Sinica, 2018, 5(6): 1025-1034.
[3] ALFEO A L, CIMINO M G C A, GAGLIARDI G. Concept-wise granular computing for explainable artificial intelligence[J]. Granular Computing, 2022, 8(4): 827-838.
[4] 高天宇, 王庆荣, 杨磊. 粗糙集属性依赖度强化的应急数据挖掘模型[J]. 计算机工程与应用, 2021, 57(3): 87-93. GAO Tianyu, WANG Qingrong, YANG Lei. Data mining model based on attribute dependability enhancement of rough set[J]. Computer Engineering and Applications, 2021, 57(3): 87-93.
[5] WANG H, GUAN J T. A dynamic framework for updating approximations with increasing or decreasing objects in multi-granulation rough sets[J]. Soft Computing, 2023, 27(9): 5257-5276.
[6] QIAN Y H, LIANG J Y, YAO Y Y, et al. MGRS: a multi-granulation rough set[J]. Information Sciences, 2010, 180(6): 949-970.
[7] SUN B Z, ZHANG X R, QI C, et al. Neighborhood relation-based variable precision multigranulation Pythagorean fuzzy rough set approach for multi-attribute group decision making[J]. International Journal of Approximate Reasoning, 2022, 151: 1-20.
[8] ZHANG X Y, JIANG J F. Measurement, modeling, reduction of decision-theoretic multigranulation fuzzy rough sets based on three-way decisions[J]. Information Sciences, 2022, 607: 1500-1582.
[9] ATEF M, ATIK E F E A. Some extensions of covering-based multigranulation fuzzy rough sets from new perspectives[J]. Soft Computing, 2021, 25(8): 6633-6651.
[10] 张明, 程科, 杨习贝, 等. 基于加权粒度的多粒度粗糙集[J]. 控制与决策, 2015, 30(2): 222-228. ZHANG Ming, CHENG Ke, YANG Xibei, et al. Multigranulation rough set based on weighted granulations[J]. Control and Decision, 2015, 30(2): 222-228.
[11] 史进玲, 张倩倩, 徐久成. 多粒度决策系统属性约简的最优粒度选择[J]. 计算机科学, 2018, 45(2), 152-156. SHI Jinling, ZHANG Qianqian, XU Jiucheng. Optimal granularity selection of attribute reductions in multi-granularity decision system[J]. Computer Science, 2018, 45(2), 152-156.
[12] 吕萍, 常玉慧, 钱进. 知识粒度框架下并行知识约简算法研究[J]. 南京大学学报(自然科学), 2022, 58(4): 594-603. LÜ Ping, CHANG Yuhui, QIAN Jin. Parallel knowledge reduction algorithm using knowledge granularity[J]. Journal of Nanjing University(Natural Science), 2022, 58(4): 594-603.
[13] 李金海, 周新然. 多粒度决策形式背景的属性约简[J]. 模式识别与人工智能, 2022, 35(5): 387-400. LI Jinhai, ZHOU Xinran. Attribute reduction in multi-granularity formal decision contexts[J]. Pattern Recognition and Artificial Intelligence, 2022, 35(5): 387-400.
[14] 桑妍丽, 钱宇华. 一种悲观多粒度粗糙集中的粒度约简算法[J]. 模式识别与人工智能, 2012, 25(3): 361-366. SANG Yanli, QIAN Yuhua. A granular space reduction approach to pessimistic multi-granulation rough sets[J]. Pattern Recognition and Artificial Intelligence, 2012, 25(3): 361-366.
[15] 桑妍丽, 钱宇华. 多粒度决策粗糙集中的粒度约简方法[J]. 计算机科学, 2017, 44(5): 199-205. SANG Yanli, QIAN Yuhua. Granular structure reduction approach to multigranulation decision-theoretic rough sets[J]. Computer Science, 2017, 44(5): 199-205.
[16] 孟慧丽, 马媛媛, 徐久成. 基于信息量的悲观多粒度粗糙集粒度约简[J]. 南京大学学报(自然科学), 2015, 51(2): 343-348. MENG Huili, MA Yuanyuan, XU Jiucheng. The granularity reduction of pessimistic multi-granulation rough set based on the information quantity[J]. Journal of Nanjing University(Natural Science), 2015, 51(2): 343-348.
[17] 孟慧丽, 马媛媛, 徐久成. 基于下近似分布粒度熵的变精度悲观多粒度粗糙集粒度约简[J]. 计算机科学, 2016, 43(2): 83-85. MENG Huili, MA Yuanyuan, XU Jiucheng. Granularity reduction of variable precision pessimistic multi-granulation rough set based on granularity entropy of lower approximate distribution[J]. Computer Science, 2016, 43(2): 83-85.
[18] 胡善忠, 徐怡, 何明慧, 等. 多粒度粗糙集粒度约简的高效算法[J]. 计算机应用, 2017, 37(12): 3391-3396. HU Shanzhong, XU Yi, HE Minghui, et al. Effective algorithm for granulation reduction of multi-granulation rough set[J]. Journal of Computer Applications, 2017, 37(12): 3391-3396.
[19] 翁冉, 王俊红, 魏巍, 等. 基于区分矩阵的多粒度属性约简[J]. 南京航空航天大学学报, 2019, 51(5): 636-641. WENG Ran, WANG Junhong, WEI Wei, et al. Multi-granulation attribute reduction based on discernibility matrix[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2019, 51(5): 636-641.
[20] 郑文彬, 李进金, 张燕兰, 等. 基于矩阵的多粒度粗糙集粒度约简方法[J]. 南京大学学报(自然科学), 2021, 51(1): 141-149. ZHENG Wenbin, LI Jinjin, ZHANG Yanlan, et al. Matrix-based granulation reduction method for multi-granulation rough sets[J]. Journal of Nanjing University(Natural Science), 2021, 51(1): 141-149.
[21] 卢加学, 汪小燕. 关于粒度重要性公式的改进[J]. 苏州科技大学学报(自然科学版), 2021, 38(4): 79-84. LU Jiaxue, WANG Xiaoyan. Improvements to the granularity importance formula[J]. Journal of Suzhou University of Science and Technology(Natural Science Edition), 2021, 38(4): 79-84.
[22] 薛占熬, 韩丹杰, 吕敏杰, 等. 一种新的基于粒度重要度的三支决策模型[J]. 计算机科学, 2019, 46(2): 236-241. XUE Zhanao, HAN Danjie, LÜ Minjie, et al. New three-way decisions model based on granularity importance degree[J]. Computer Science, 2019, 46(2): 236-241.
[23] 杨习贝, 颜旭, 徐苏平, 等. 基于样本选择的启发式属性约简方法研究[J]. 计算机科学, 2016, 43(1): 40-43. YANG Xibei, YAN Xu, XU Suping, et al. New heuristic attribute reduction algorithm based on sample selection[J]. Computer Science, 2016, 43(1): 40-43.
[24] XIAO Z, CHANG L M, DE G C, et al. Feature selection in mixed data: a method using a novel fuzzy rough set-based information entropy[J]. Pattern Recognition, 2016, 56(1): 1-15.
[25] WANG C Z, HU Q H, WANG X Z, et al. Feature selection based on neighborhood discrimination index[J]. IEEE Trans on Neural Networks and Learning Systems, 2018, 29(7): 2986-2999.
[26] 张昭琴, 徐泰华, 鞠恒荣, 等. 基于粒度的加速求解约简策略[J]. 南京理工大学学报, 2021, 45(4): 401-408. ZHANG Zhaoqin, XU Taihua, JU Hengrong, et al. Accelerative solving reduct strategy based on granularity[J]. Journal of Nanjing University of Science and Technology, 2021, 45(4): 401-408.
[1] 樊伟. 一种多粒度粗糙区间模糊集方法[J]. 山东大学学报(工学版), 2013, 43(1): 63-68.
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