基于快速超粒方生成算法的分类器模型

doi:10.6040/j.issn.1671-9352.5.2025.006

摘要/Abstract

摘要： 在空间划分时粒球计算方法存在半径敏感性、覆盖盲区与区域重叠缺陷等问题,本文提出基于n维超长方体的信息粒化方法。突破传统球形结构约束,采用n维超长方体几何模型,建立无盲区、无重叠的空间划分理论体系,提出快速超粒方生成(fast granular hypercube generation, FGHG)算法,通过维度自适应分割机制实现高效空间划分,与传统粒球生成算法相比,FGHG算法在计算效率方面具有显著优势,设计快速超粒方分类器(fast granular hypercube classifier, FGHC)。为验证所提算法的有效性,选取13个真实数据集评估,FGHC算法的分类精度和F1分数均较高。本文建立的超粒方计算范式,为解决复杂数据空间划分问题提供新的理论框架。

关键词: 粒计算, 超粒方, 信息粒化, 分类器, 粒球计算

Abstract: To address the problem of radius sensitivity in granular ball based spatial partitioning, which results in coverage gaps or region overlaps, an information granulation method is proposed based on n-dimensional hypercubes. The traditional constraint of spherical structures is overcome by introducing a novel n-dimensional hypercube geometric model, which establishes a theoretical framework for spatial partitioning without coverage gaps or overlapping regions. A fast granular hypercube generation(FGHG)algorithm is proposed, which utilizes a dimension-adaptive partitioning mechanism to enable efficient spatial division. Compared with traditional granular ball generation algorithms, FGHG algorithm demonstrates significant advantages in computational efficiency. A fast granular hypercube classifier(FGHC)is designed. To validate the effectiveness of the proposed algorithm, a systematic evaluation is conducted on 13 real-world datasets from the repository, where FGHC algorithm achieving improvements in both classification accuracy and F1 score. The granular hypercube computing paradigm established in this study provides a novel theoretical framework for tackling complex spatial partitioning problems in data analysis.

Key words: granular computing, granular hypercube, information granulation, classifier, granular ball computing

中图分类号:

TP311

何怡,邵亚斌,冯慧,郭瑞莲. 基于快速超粒方生成算法的分类器模型[J]. 《山东大学学报(理学版)》, 2026, 61(5): 65-78.

HE Yi, SHAO Yabin, FENG Hui, GUO Ruilian. A classifier model based on the fast granular hypercube generation algorithm[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2026, 61(5): 65-78.

参考文献

[1] YAO Jingtao, ATHANASIOS V, WITOLD P. Granular computing: perspectives and challenges[J]. IEEE Transactions on Cybernetics, 2013, 43(6):1977-1989.
[2] 王国胤,张清华,胡军. 粒计算研究综述[J]. 智能系统学报,2007,2(6):8-26. WANG Guoyin, ZHANG Qinghua, HU Jun. An overview of granular computing[J]. CAAI Transactions on Intelligent Systems, 2007, 2(6):8-26.
[3] 张清华,王宇泰,赵凡. 复杂问题求解的多粒度计算框架[J]. 中国科学:信息科学,2025,55(5):1122-1139. ZHANG Qinghua, WANG Yutai, ZHAO Fan. Multi-granularity computing framework for complex problem solving[J]. Scientia Sinica Informations, 2025, 55(5):1122-1139.
[4] ZADEH L A. Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic[J]. Fuzzy Sets and Systems, 1997, 90(2):111-127.
[5] SARKAR M. Fuzzy-rough nearest neighbor algorithms in classification[J]. Fuzzy Sets and Systems, 2007, 158(19):2134-2152.
[6] XIA Shuyin, ZHENG Shaoyuan, WANG Guoyin, et al. Granular ball sampling for noisy label classification or imbalanced classification[J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 34(4):2144-2155.
[7] QUADIR A, TANVEER M. Granular ball twin support vector machine with pinball loss function[J]. IEEE Transactions on Computational Social Systems, 2024: 1-10.
[8] SAJID M, QUADIR A, TANVEER M, et al. GB-RVFL: fusion of randomized neural network and granular ball computing[J]. Pattern Recognition, 2025, 159:111142.
[9] 华有霖,邵亚斌,朱学勤. 基于粒球计算的多粒度支持向量回归算法[J]. 山东大学学报(理学版),2025,60(7):1-12. HUA Youlin, SHAO Yabin, ZHU Xueqin. Multi-granularity support vector regression algorithm based on granular ball computing[J]. Journal of Shandong University(Natural Science), 2025, 60(7):1-12.
[10] 薛任煊,伊士超,王平心. GBDEN:一种基于粒球的大规模数据快速聚类方法[J]. 计算机科学,2024,51(12):166-173. XUE Renxuan, YI Shichao, WANG Pingxin. GBDEN: a fast clustering algorithm for large-scale data based on granular ball[J]. Computer Science, 2024, 51(12):166-173.
[11] PENG Xiaoli, WANG Ping, XIA Shuyin, et al. VPGB: a granular-ball based model for attribute reduction and classification with label noise[J]. Information Sciences, 2022, 611:504-521.
[12] CHENG Dongdong, LI Ya, XIA Shuyin, et al. a fast granular-ball-based density peaks clustering algorithm for large-scale data[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(12):17202-17215.
[13] GILET C, BARBOSA S, FILLATRE L. Discrete box-constrained minimax classifier for uncertain and imbalanced class proportions[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(6):2923-2937.
[14] WU Chengying, ZHANG Qinghua, YIN Longjun, et al. Data-driven interval granulation approach based on uncertainty principle for efficient classification[J]. IEEE Transactions on Fuzzy Systems, 2023, 32(1):12-26.
[15] XIA Shuyin, LIU Yunsheng, DING Xin, et al. Granular ball computing classifiers for efficient, scalable and robust learning[J]. Information Sciences, 2019, 483:136-152.
[16] XIA Shuyin, DAI Xiaochuan, WANG Guoyin, et al. An efficient and adaptive granular-ball generation method in classification problem[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 35(4):5319-5331.
[17] CHENG Dongdong, ZHANG Cheng, LI Ya, et al. GB-DBSCAN: a fast granular-ball based dbscan clustering algorithm[J]. Information Sciences, 2024, 674:120731.
[18] SHAO Yabin, HUA Youlin, GONG Zengtai, et al. CON-MGSVM: controllable multi-granularity support vector algorithm for classification and regression[J]. Information Fusion, 2025,117:102867.
[19] XIE Qin, ZHANG Qinghua, XIA Shuyin, et al. GBG++: a fast and stable granular ball generation method for classification[J]. IEEE Transactions on Emerging Topics in Computational Intelligence, 2024, 8(2):2022-2036.
[20] JODAS D, PASSOS L, ADEEL A, et al. PL-KNN: a python-based implementation of a parameterless K-nearest neighbors classifier[J]. Software Impacts, 2023, 15:100459.
[21] LI Chen, SHAO Yabin, XIA Shuyin, et al. An adaptive granular ball classifier based on natural neighbor[C] //Proceedings of the 2023 8th International Conference on Mathematics and Artificial Intelligence. New York: ACM, 2023:47-52.
[22] XIA Shuyin, LIAN Xiaoyu, WANG Guoyin, et al. GBSVM: an efficient and robust support vector machine framework via granular-ball computing[J]. IEEE Transactions on Neural Networks and Learning Systems, 2024, 36(5):9253-9267.
[23] XIE Jiang, XIANG Xuexin, XIA Shuyin, et al. MGNR: a multi-granularity neighbor relationship and its application in KNN classification and clustering methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024, 46(12):7956-7972.
[24] GANAIE M, VRUSHANK A, ANOUCK G. Granular ball K-class twin support vector classifier[J]. Pattern Recognition, 2025, 116:111636.
[25] 邓波军,吴南海,陈玉明,等. 旋转粒支持向量机分类器算法[J]. 山东大学学报(理学版),2026,61(5):102-113.

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