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Title of Journal: Soft Comput

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Abbravation: Soft Computing

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Springer Berlin Heidelberg

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DOI

10.1016/0167-5877(84)90020-5

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1433-7479

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A robust algorithm of support vector regression wi

Authors: Chuanfa Chen Changqing Yan Na Zhao Bin Guo Guolin Liu
Publish Date: 2016/06/22
Volume: 21, Issue: 18, Pages: 5235-5243
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Abstract

Support vector machine for regression SVR is an efficient tool for solving function estimation problem However it is sensitive to outliers due to its unbounded loss function In order to reduce the effect of outliers we propose a robust SVR with a trimmed Huber loss function SVRT in this paper Synthetic and benchmark datasets were respectively employed to comparatively assess the performance of SVRT and its results were compared with those of SVR least squares SVR LSSVR and a weighted LSSVR The numerical test shows that when training samples are subject to errors with a normal distribution SVRT is slightly less accurate than SVR and LSSVR yet more accurate than the weighted LSSVR However when training samples are contaminated by outliers SVRT has a better performance than the other methods Furthermore SVRT is faster than the weighted LSSVR Simulating eight benchmark datasets shows that SVRT is averagely more accurate than the other methods when sample points are contaminated by outliers In conclusion SVRT can be considered as an alternative robust method for simulating contaminated sample pointsThis work is funded by National Natural Science Foundation of China Grant Nos 41371367 41101433 by SDUST Research Fund by Joint Innovative Center for Safe And Effective Mining Technology and Equipment of Coal Resources Shandong Province and by Special Project Fund of Taishan Scholars of Shandong Province

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