[1]张光晨,李展菲,何舒平,等.基于SMC策略的线性不可分数据集的分类算法[J].郑州大学学报(工学版),2026,47(XX):1-6.[doi:10.13705/j.issn.1671-6833.2026.04.004]
　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]
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基于SMC策略的线性不可分数据集的分类算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
47
期数:
2026年XX
页码:
1-6
栏目:
出版日期:
2026-09-10

文章信息/Info

Title:
Classification Algorithm for Linearly Inseparable Datasets Based on SMC Strategy
作者:
张光晨1; 李展菲1; 何舒平2; 夏元清3
1. 北方民族大学 数学与信息科学学院, 宁夏 银川 750021;2. 安徽大学 电气工程与自动化学院, 安徽 合肥230601;3. 北京理工大学 自动化学院,北京 100081
Author(s):
ZHANG Guangchen1;  LI Zhanfei1;  HE Shuping2;  XIA Yuanqing3
1. School of Mathematics and Information Science, North Minzu University, Yinchuan 750021, China; 2. School of Electrical Engineering and Automation,Anhui University, Hefei 230601, China; 3. School of Automation, Beijing University of Technology, Beijing 100081, China
关键词:
支持向量机; 滑模控制策略; 分类算法; 核函数; 参数
Keywords:
support vector machines; sliding mode control strategy; classification algorithm; nuclear function; parameter
分类号:
TP181；O224
DOI:
10.13705/j.issn.1671-6833.2026.04.004
文献标志码:
A
摘要:
针对线性不可分数据集的分类问题，通过将滑模控制（SMC）思想用于支持向量（SVM）核函数参数优化过程，提出一种基于SMC策略的SVM核函数参数优化算法。设计误差方程和滑模面，建立SVM分类目标函数与SMC的关联，并推导出核函数参数迭代更新规则及代价函数。通过动态调整SVM核函数参数，在减少支持向量数量的同时提升分类性能。实验部分采用Iris、Heart disease等6个UCI数据集验证算法有效性。结果显示，与传统SVM相比，所提算法在Iris数据集上支持向量减少56.25%，测试准确率保持100%；在Heart-disease数据集上测试准确率提升13.58百分点。所提算法与现有优化算法对比，在多个数据集上表现出更高的分类精度。
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.

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备注/Memo

备注/Memo:
收稿日期:2025-09-01;修订日期:2025-10-20
基金项目：国家自然科学基金项目(62563002)；宁夏自然科学基金优秀青年项目(2022AAC05039)；北方民族大学创新项目(YCX24288)。
作者简介：张光晨(1985— ) ,男,山西晋中人,北方民族大学副教授,博士,主要从事复杂网络智能控制、数据分析与决策、大模型理论及应用研究，E-mail：guangchen_123@163.com。
更新日期/Last Update: 2026-01-13