Support vector regression model with variant tolerance

Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius ε -tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors ac...

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Bibliographic Details
Published in:Measurement and control (London) Vol. 56; no. 9-10; pp. 1705 - 1719
Main Authors: Wei, Jiangyue, He, Xiaoxia
Format: Journal Article
Language:English
Published: London, England SAGE Publications 01.11.2023
Sage Publications Ltd
SAGE Publishing
Subjects:
Algorithms
Data points
Datasets
Outliers (statistics)
Performance prediction
Regression models
Support vector machines
non-deterministic algorithm
Support Vector Regression
Alterable εi
ISSN:0020-2940, 2051-8730
Online Access:Get full text
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Summary:Most works on Support Vector Regression (SVR) focus on kernel or loss functions, with the corresponding support vectors obtained using a fixed-radius ε -tube, affording good predictive performance on datasets. However, the fixed radius limitation prevents the adaptive selection of support vectors according to the data distribution characteristics, compromising the performance of the SVR-based methods. Therefore, this study proposes an “Alterable ε i -Support Vector Regression” ( A ε i -SVR) model by applying a novel ε , named “Alterable ε i ,” to the SVR model. Based on the data point sparsity at each location, the model solves the different ε i at the corresponding position, and thus zoom-in or zoom-out the ε -tube by changing its radius. Such a variable ε -tube strategy diminishes noise and outliers in the dataset, enhancing the prediction performance of the A ε i -SVR model. Therefore, we suggest a novel non-deterministic algorithm to iteratively solve the complex problem of optimizing ε i associated with every location. Extensive experimental results demonstrate that our approach can improve the accuracy and stability on simulated and real data compared with the baseline methods.
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ISSN:0020-2940
2051-8730
DOI:10.1177/00202940231180620