Multi-granulation rough set model based on granular-ball computing

JIANG Shanshan; LIN Guoping; LIN Yidong; KOU Yi

doi:10.16152/j.cnki.xdxbzr.2024-02-006

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Multi-granulation rough set model based on granular-ball computing

更新时间：2024-04-19

- Multi-granulation rough set model based on granular-ball computing
- Journal of Northwest University (Natural Science Edition) Vol. 54, Issue 2, Pages: 197-208(2024)
- 作者机构：
  
  1.闽南师范大学　数学与统计学院，福建　漳州　363000
  2.数字福建气象大数据研究所，福建　漳州　363000
  3.福建省粒计算及其应用重点实验室，福建　漳州　363000
- 作者简介：
- 基金信息：
- DOI：10.16152/j.cnki.xdxbzr.2024-02-006
  CLC： TP391
- Published：25 April 2024，
  
  Received：15 September 2023，
扫描看全文
JIANG Shanshan, LIN Guoping, LIN Yidong, et al. Multi-granulation rough set model based on granular-ball computing. [J]. Journal of Northwest University (Natural Science Edition) 54(2):197-208(2024)
DOI：

JIANG Shanshan, LIN Guoping, LIN Yidong, et al. Multi-granulation rough set model based on granular-ball computing. [J]. Journal of Northwest University (Natural Science Edition) 54(2):197-208(2024) DOI： 10.16152/j.cnki.xdxbzr.2024-02-006.

摘要

基于粒球计算的粗糙集理论作为知识发现和数据挖掘的重要工具之一，已成功地应用于标记预测、属性约简等。而现有的粒球粗糙集模型仅仅是从单粒度出发，无法从多粒度角度对数据进行分析和处理，实际生活中仍有很多应用场景需从多粒度角度进行思考。将粒球计算思想结合到多粒度粗糙集模型，提出了多粒度粒球粗糙集模型，并讨论了该模型的相关性质。该模型通过纯度的设定对数据进行粒球划分，能够有效地刻画数据之间的内在联系，以此设计多粒度粒球粗糙集的正域生成算法。实验分析表明该模型的可行性和有效性。

Abstract

As one of the important tools for knowledge discovery and data mining

rough set theory based on granular-ball computing has been successfully applied to label prediction and attribute reduction. However

the existing granular-ball rough set models only consider a single granulation

and cannot analyze and process data from a multi-granulation

and there are still many application scenarios that need to be considered from the perspective of multi-granulation. Based on this

this paper proposes a multi-granulation rough set based on granular-ball computing by embedding the idea of granular-ball in the multi-granulation rough set model

and discusses the relevant properties of the model. The model divides the data by setting the purity

which can effectively depict the internal relationship between the data

and thus design a position region generation algorithm for multi-granulation granular-ball rough set. Experimental analysis shows the feasibility and effectiveness of this model.

关键词

粒球计算粒球粗糙集多粒度粗糙集纯度

Keywords

granular-ball computinggranular-ball rough setmulti-granulation rough setpurity

references

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

WEI J M, WANG S Q, YUAN X J. Ensemble rough hypercuboid approach for classifying cancers[J]. IEEE Transactions on Knowledge and Data Engineering, 2010, 22(3): 381-391.

马文萍, 黄媛媛, 李豪, 等. 基于粗糙集与差分免疫模糊聚类算法的图像分割[J]. 软件学报, 2014, 25(11): 2675-2689.

MA W P, HUANG Y Y, LI H, et al. Image segmentation based on rough set and differential immune fuzzy clustering algorithm[J]. Journal of Software, 2014, 25(11): 2675-2689.

QIAN Y H, XU H, LIANG J Y, et al. Fusing monotonic decision trees[J]. IEEE Transactions on Knowledge and Data Engineering, 2015, 27(10): 2717-2728.

QIAN Y H, LIANG X Y, WANG Q, et al. Local rough set: A solution to rough data analysis in big data[J]. International Journal of Approximate Reasoning, 2018, 97: 38-63.

VIDHYA K A, GEETHA T V. Entity resolution framework using rough set blocking for heterogeneous web of data[J]. Journal of Intelligent & Fuzzy Systems, 2018, 34(1): 659-675.

HU M J, YAO Y Y. Structured approximations as a basis for three-way decisions in rough set theory[J]. Knowledge-Based Systems, 2019, 165: 92-109.

梁吉业, 钱宇华, 李德玉, 等. 面向大数据的粒计算理论与方法研究进展[J]. 大数据, 2016, 2(4): 13-23.

LIANG J Y, QIAN Y H, LI D Y, et al. Research development on granular computing theory and method for big data[J]. Big Data research, 2016, 2(4): 13-23.

KRYSZKIEWICZ M. Rough set approach to incomplete information systems[J]. Information Sciences, 1998, 112(1/2/3/4): 39-49.

王国胤. Rough集理论在不完备信息系统中的扩充[J] 计算机研究与发展, 2002, 39(10): 1238-1243.

WANG G Y. Extension of rough set under incomplete information systems[J]. Journal of Computer Research and Development, 2002, 39(10): 1238-1243.

STEFANOWSKI J, TSOUKIAS A. Incomplete information tables and rough classification[J] Computational Intelligence, 2001, 17(3): 545-566.

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.

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, 2010, 40(2): 420-431.

QIAN Y H, LI S Y, LIANG J Y, et al. Pessimistic rough set based decisions: A multigranulation fusion strategy[J]. Information Sciences, 2014, 264: 196-210.

HU Q H, YU D R, XIE Z X. Neighborhood classifiers[J]. Expert Systems with Applications, 2008, 34(2): 866-876.

李楠, 谢娟英. 基于邻域粗糙集的增量特征选择[J]. 计算机技术与发展, 2011, 21(11): 149-152.

LI N, XIE J Y. A feature subset selection algorithm based on neighborhood rough set for incremental updating datasets[J]. Computer Technology and Development, 2011, 21(11): 149-152.

胡清华, 赵辉, 于达仁. 基于邻域粗糙集的符号与数值属性快速约简算法[J]. 模式识别与人工智能, 2008, 21(6): 732-738.

HU Q H, ZHAO H, YU D R. Efficient symbolic and numerical attribute reduction with neighborhood rough sets[J]. Pattern Recognition and Artificial Intelligence, 2008, 21(6):732-738.

彭潇然, 刘遵仁, 纪俊. 自适应的邻域粗糙集邻域大小取值方法[J]. 计算机应用研究, 2019, 36(1): 144-147.

PENG X R, LIU Z R, JI J. Adaptable method for determining neighborhood size of neighborhood rough set[J]. Application Research of Computers, 2019, 36(1): 144-147.

XIA S Y, LIU Y S, DING X, et al. Granular ball computing classifiers for efficient scalable and robust learning[J]. Information Sciences, 2019, 483(1): 136-152.

XIA S Y, WANG C, WANG G Y, et al.GBRS: An unified model of Pawlak rough set and neighborhood rough set[EB/OL]. (2022-01-10)[2023-06-01]. http://arxiv.org/abs/2201.03349http://arxiv.org/abs/2201.03349.

XIA S Y, PENG D W, MENG D Y, et al. Ball k-means: Fast adaptive clustering with no bounds[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(1): 87-99.

XIA S Y, ZHANG H, LI W H, et al. GBNRS: A novel rough set algorithm for fast adaptive attribute reduction in classification[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(3): 1231-1242.

XIA S Y, ZHENG S Y, WANG G Y, et al.Granular ball sampling for noisy label classification or imbalanced classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2023, 34(4): 2144-2155.

陈中华, 巴婧, 徐泰华, 等. 一种面向粒球粗糙集的快速约简求解方法[J]. 小型微型计算机系统, 2023, 44(1): 24-29.

CHEN Z H, BA J, XU T H, et al. Quick strategy for searching granular ball rough set based reduct[J]. Journal of Chinese Computer Systems, 2023, 44(1): 24-29.

XIE J, XIA S Y, WANG G Y, et al. GBMST: An efficient minimum spanning tree clustering based on granular-ball computing[EB/OL]. (2023-03-02)[2023-06-01]. http://arxiv.org/abs/2303.01082http://arxiv.org/abs/2303.01082.

梁吉业, 徐宗本, 李月香. 包含度与粗糙集数据分析中的度量[J]. 计算机学报, 2001, 24(5): 544-547.

LIANG J Y, XU Z B, LI Y X. Inclusion degree and measures of rough set data analysis[J]. Chinese Journal of Computers, 2001, 24(5): 544-547.

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