Classification Algorithm for Linearly Inseparable Datasets Based on SMC Strategy
[1]ZHANG Guangchen,LI Zhanfei,HE Shuping,et al.Classification Algorithm for Linearly Inseparable Datasets Based on SMC Strategy[J].Journal of Zhengzhou University (Engineering Science),2026,47(XX):1-6.[doi:10.13705/j.issn.1671-6833.2026.04.004]
Copy
Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
47
Number of periods:
2026 XX
Page number:
1-6
Column:
Public date:
2026-09-10
- Title:
- Classification Algorithm for Linearly Inseparable Datasets Based on SMC Strategy
- Keywords:
- support vector machines; sliding mode control strategy; classification algorithm; nuclear function; parameter
- CLC:
- TP181；O224
- Abstract:
- For the classification problem of linearly inseparable datasets, a support vector machine (SVM) kernel function parameter optimization algorithm was proposed based on the sliding mode control (SMC) strategy by applying the SMC idea to the SVM kernel function parameter optimization process. Designing the error equation and sliding surface, the association between SVM classification objective function and SMC was established, and the iterative update rules of kernel function parameters and cost function were derived. The algorithm improved classification performance while reducing the number of support vectors by dynamically adjusting the kernel parameters of SVM. In the experiment part, six UCI datasets, such as Iris and Heart disease, were used to verify the validity of the algorithm. The results showed that compared with traditional SVM, the proposed algorithm reduced the number of support vectors by 56.25% on the Iris dataset, and the test accuracy remained at 100%. Test accuracy increased by 13.58 percentage points on the Heart disease dataset. Furthermore, the proposed algorithm, compared with existing optimization algorithms, showed a higher classification accuracy on some datasets.
- References:
-
[1] BISHOP C M. Pattern recognition and machine learning [M]. New York: Springer, 2006.
[2] 周志华. 机器学习 [M]. 北京: 清华大学出版社, 2016.
ZHOU Z H. Machine learning [M]. Beijing: Tsinghua University Press, 2016.
[3] 李晓桐, 程璠, 田晶, 等. 结合 SMOTE 技术与优化算法的支持向量机在慢性心衰不良结局预测中的应用[J]. 中国卫生统计, 2024, 41(6): 802-806.
LI X T, CHENG F, TIAN J. Application of SMOTE technique and optimization algorithm support vector machine in predicting adverse outcome of chronic heart failure[J]. Chinese Journal of Health Statistics, 2024, 41(6): 802-806.
[4] 郑祥, 张鸿鹄, 魏岩岩. 基于改进 SVM 的变电站设备红外图像故障诊断方法研究[J]. 电测与仪表, 2024, 61(12): 49-55.
ZHENG X, ZHANG H H, WEI Y Y. Research on infrared image fault diagnosis method for substation equipment based on improved SVM[J]. Electrical Measurement & Instrumentation, 2024, 61(12): 49-55.
[5] 赵旭阳, 张延彬, 王忠勇, 等. 基于 SVM 的声磁标签检测系统设计及其 FPGA 实现[J]. 郑州大学学报(工学版), 2021, 42(3): 13-18.
ZHAO X Y, ZHANG Y B, WANG Z Y, et al. Design of acoustic magnetic label detection system based on SVM and FPGA implementation[J]. Journal of Zhengzhou University (Engineering Science), 2021, 42(3): 13-18.
[6] ADEYANJU I A, OMIDIORA E O, OYEDOKUN O F. Performance evaluation of different support vector machine kernels for face emotion recognition[C]//2015 SAI Intelligent Systems Conference (IntelliSys). Piscataway: IEEE, 2015: 804-806.
[7] ZHOU P J, ZENG W T, ZHANG W W, et al. Multicavitation states identification of a sewage pump using CEEMDAN and BOA-SVM[J]. Journal of Water Process Engineering, 2024, 61: 105299.
[8] ZHANG H, ZHANG Y F. An improved sparrow search algorithm for optimizing support vector machines[J]. IEEE Access, 2023, 11: 8199-8206.
[9] ABBASZADEH M, SOLTANI-MOHAMMADI S, AHMED A N. Optimization of support vector machine parameters in modeling of Iju deposit mineralization and alteration zones using particle swarm optimization algorithm and grid search method[J]. Computers & Geosciences, 2022, 165: 105140.
[10] LI C P, PENG T H, ZHU Y M. A cutting pattern recognition method for shearers based on CEEMDAN and improved grey wolf optimizer algorithm-optimized SVM[J]. Applied Sciences, 2021, 11(19): 9081.
[11] ZHANG G C, XIA Y Q, LI X F, et al. Multievent-triggered sliding-mode control for a class of complex dynamic network[J]. IEEE Transactions on Control of Network Systems, 2022, 9(2): 835-844.
[12] 刘凌, 司杰文, 林起联. 支持向量机预测可变参数的永磁同步电机快速终端滑模控制[J]. 西安交通大学学报, 2021, 55(6): 53-60.
LIU L, SI J W, LIN Q L. A fast terminal sliding mode control of permanent magnet synchronous motor with variable parameters predicted by SVM[J]. Journal of Xi’an Jiaotong University, 2021, 55(6): 53-60.
[13] YALSAVAR M, KARIMAGHAEE P, SHEIKH-AKBARI A, et al. Sliding mode control based support vector machine RBF kernel parameter optimization[C]//2019 IEEE International Conference on Imaging Systems and Techniques (IST). Piscataway: IEEE, 2019: 1-6.
[14] 宋慧玲, 张仲广, 夏冰. 基于支持向量机的分类问题研究[M]. 哈尔滨: 黑龙江大学出版社, 2022.
SONG H L, ZHANG Z G, XIA B. Research on classification problem based on support vector machine[M]. Harbin: Heilongjiang University Press, 2022.
[15] 蒋刚. 核函数理论与信号处理[M]. 北京: 科学出版社, 2013.
JIANG G. Kernel function theory and signal processing[M]. Beijing: Science Press, 2013.
[16] CRISTIANINI N, SHAWE-TAYLOR J. An introduction to support vector machines and other kernel-based learning methods[M]. New York: Cambridge University Press, 2000.
[17] RASMUSSEN C E, WILLIAMS C K I. Gaussian processes for machine learning[M]. Cambridge: MIT Press, 2006.
[18] FISHER R A. Iris [DS/OL]. (1988-06-30)[2025-08-11]. https://doi.org/10.24432/C56C76.
[19] JANOSI A, STEINBRUNN W, PFISTERER M, et al. Heart disease [DS/OL]. (1988-06-30)[2025-08-11]. https://doi.org/10.24432/C52P4X.
[20] HABERMAN S. Haberman’s survival [DS/OL]. (1999-03-20)[2025-08-11]. https://doi.org/10.24432/C5XK51.
[21] SLATE D. Letter recognition [DS/OL]. (1990-12-31)[2025-08-11]. https://doi.org/10.24432/C5ZP40.
[22] SEJNOWSKI T, GORMAN R. Connectionist bench (sonar, mines vs. rocks) [DS/OL]. [2025-08-11]. https://doi.org/10.24432/C5T01Q.
[23] WNEK J. MONK’s problems [DS/OL]. (1992-09-30)[2025-08-11]. https://doi.org/10.24432/C5R30R.
- Similar References:
Memo
-
Last Update:
2026-01-13
