ISGS: A Combinatorial Model for Hysteresis Effects

FENG Ting-ting; PENG Yan; WANG Jie

doi:10.12263/DZXB.20220238

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ISGS: A Combinatorial Model for Hysteresis Effects

PAPERS | 更新时间：2025-12-08

- ISGS: A Combinatorial Model for Hysteresis Effects
- ACTA ELECTRONICA SINICA Vol. 51, Issue 9, Pages: 2504-2509(2023)
- 作者机构：
  
  首都师范大学管理学院，北京 100056
- 作者简介：
- 基金信息：
  
  National Education Science Plan-Key project of the Ministry of Education(DLA190426)
- DOI：10.12263/DZXB.20220238
  CLC： TP181
- Received：09 March 2022，
  
  Revised：2022-07-30，
  
  Published：25 September 2023
- 稿件说明：
移动端阅览
冯婷婷,彭岩,王洁.ISGS：一种面向滞后效应的组合模型研究[J].电子学报,2023,51(09):2504-2509.

FENG Ting-ting,PENG Yan,WANG Jie.ISGS: A Combinatorial Model for Hysteresis Effects[J].ACTA ELECTRONICA SINICA,2023,51(09):2504-2509.
冯婷婷,彭岩,王洁.ISGS：一种面向滞后效应的组合模型研究[J].电子学报,2023,51(09):2504-2509. DOI： 10.12263/DZXB.20220238.

FENG Ting-ting,PENG Yan,WANG Jie.ISGS: A Combinatorial Model for Hysteresis Effects[J].ACTA ELECTRONICA SINICA,2023,51(09):2504-2509. DOI： 10.12263/DZXB.20220238.

摘要

针对滞后效应明显、样本量小的数据集，为解决单一算法模型预测精度低、泛化能力差的问题，提出了一种基于等距特征映射算法（Isometric Feature Mapping， ISOMAP）、少数类过采样技术（Synthetic Minority Oversampling Technique， SMOTE）、遗传算法（Genetic Algorithm， GA）、支持向量回归（Support Vector Regression， SVR）的组合模型ISGS（ISOMPA-SMOTE-GA-SVR）. 首先，利用ISOMAP和SMOTE算法对滞后效应明显、样本量较小的数据集进行特征变换. 其次，利用SVR算法较强的非线性分类能力及泛化能力对数据集进行回归分析. 最后，利用GA算法对SVR算法的参数进行优化，以提升模型的预测精度. 采用气象因素、空气质量、呼吸系统发病人数三组数据集，基于ISGS模型进行了发病人数预测的仿真实验和对比实验. 实验结果表明，该模型预测精度和准确率较传统模型均有所提高，预测精度达到93.65%（传统单一模型83.481%）. 同时具有更好的泛化能力，能够更好地处理高维度、小样本数据集.

Abstract

In anticipation of data sets with small sample size and evident lag effects

a novel ISGS (ISOMPA-SMOTE-GA-SVR) model was proposed to address the issues of low prediction accuracy and inadequate generalization in single-algorithm prediction models. This ISGS model integrates isometric feature mapping (ISOMAP)

synthetic minority oversampling technique (SMOTE)

genetic algorithm (GA)

and support vector regression (SVR)

thereby providing a comprehensive solution.Firstly

ISOMAP and SMOTE were employed to perform feature transformation on data sets characterized by small sample size and evident lag. Secondly

the SVR algorithm was adopted due to its robust ability to generalize and classify non-linearly in regression analysis of the data set. Lastly

GA was utilized to optimize the parameters of SVR

thereby enhancing the prediction accuracy of the model. Three data sets comprised of meteorological factors

air quality and the number of patients with respiratory diseases was utilized to conduct simulation and comparative experiments using the ISGS model. The experimental results demonstrate that the proposed ISGS model achieves a prediction accuracy of 93.65%

surpassing that of all other reference models. Furthermore

the model exhibits superior generalization capabilities and can effectively handle data sets with higher dimension and smaller sample size.

关键词

Keywords

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