基于最小二乘孪生支持向量机的不确定数据学习算法

刘锦能; 肖燕珊; 刘波

doi:10.12052/gdutxb.220027

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基于最小二乘孪生支持向量机的不确定数据学习算法

计算机科学与技术 | 更新时间：2026-05-27

- 基于最小二乘孪生支持向量机的不确定数据学习算法
- A Least Squares Twin Support Vector Machine Method with Uncertain Data
- 广东工业大学学报 2024年41卷第1期页码：79-85
- 作者机构：
  
  1. 广东工业大学计算机学院,广东,广州,510006
  2. 广东工业大学自动化学院,广东,广州,510006
- 作者简介：
  
  刘锦能(1996–) ，男，硕士研究生，主要研究方向为支持向量机
  刘波(1978–) ，男，教授，博士，主要研究方向为机器学习、数据挖掘，E-mail：csboliu@163.com
- 基金信息：
  
  国家自然科学基金资助项目(62076074)
- DOI：10.12052/gdutxb.220027
  中图分类号： TP391
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
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刘锦能, 肖燕珊, 刘波. 基于最小二乘孪生支持向量机的不确定数据学习算法[J]. 广东工业大学学报, 2024, 41(1): 79-85.

Liu Jin-neng, Xiao Yan-shan, Liu Bo. A Least Squares Twin Support Vector Machine Method with Uncertain Data[J]. 2024, 41(1): 79-85.
刘锦能, 肖燕珊, 刘波. 基于最小二乘孪生支持向量机的不确定数据学习算法[J]. 广东工业大学学报, 2024, 41(1): 79-85. DOI： 10.12052/gdutxb.220027.

Liu Jin-neng, Xiao Yan-shan, Liu Bo. A Least Squares Twin Support Vector Machine Method with Uncertain Data[J]. 2024, 41(1): 79-85. DOI： 10.12052/gdutxb.220027.

摘要

孪生支持向量机通过计算2个二次规划问题，得到2个不平行的超平面，用于解决二分类问题。然而在实际的应用中，数据通常包含不确定信息，这将会对构建模型带来困难。对此，提出了一种用于求解带有不确定数据的最小二乘孪生支持向量机模型。首先，对于每个实例，该方法都分配一个噪声向量来构建噪声信息。其次，将噪声向量结合到最小二乘孪生支持向量机，并在训练阶段得到优化。最后，采用一个2步循环迭代的启发式框架求解得到分类器和更新噪声向量。实验表明，跟其他对比方法比较，本方法采用噪声向量对不确定信息进行建模，并将孪生支持向量机的二次规划问题转化为线性方程，具有更好的分类精度和更高的训练效率。

Abstract

Twin support vector machine learns two nonparallel hyperplanes by calculating two quadratic programming problems to solve the binary classification problems. However

in practical applications

the data usually contain uncertain information

making it difficult to construct the classification model. This paper proposed a new and efficient uncertain-data-based least squares twin support vector machine (ULSTSVM) method to address the problem of data uncertainty. Firstly

since the data may contain uncertain information

a noise vector was introduced to model the uncertain information of each example. Secondly

the noise vectors were incorporated into the least squares TWSVM. Finally

to solve the derived learning problem

we employed a two-step heuristic framework to train the least squares TWSVM classifier and updated the noise vectors alternatively. The experiments showed that our proposed ULSTSVM outperforms the baselines in training time and meanwhile achieves comparable classification accuracy. In sum

ULSTSVM adopts a noise vector to model the uncertain information and transforms the quadratic programming problems of TWSVM into linear equations

such that better classification accuracy and higher training efficiency can be obtained.

关键词

Keywords

references

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