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Least absolute deviation-based robust support vector regression
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DOI：10.1016/j.knosys.2017.06.009.png)
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Abstract
En 中文
To suppress the influence of outliers on function estimation, we propose a least absolute deviation (LAD) based robust support vector regression (SVR). Furthermore, an efficient algorithm based on the split-Bregman iteration is introduced to solve the optimization problem of the proposed algorithm. Both artificial and benchmark datasets are employed to compare the performance of the proposed algorithm with those of least squares SVR (LS-SVR), and two weighted versions of LS-SVR with the weight functions of Hampel and Logistic, respectively. Experiments demonstrate the superiority of the proposed algorithm. (C) 2017 Elsevier B.V. All rights reserved.
Keywords:
Support vector regression
Robust
Outlier
Least absolute deviation
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