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Algorithms 2018, 11(5), 62; https://doi.org/10.3390/a11050062

A Feature-Weighted SVR Method Based on Kernel Space Feature

School of Automatic, Northwestern Polytechnical University, Xi’an 710072, China
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Received: 24 March 2018 / Revised: 23 April 2018 / Accepted: 2 May 2018 / Published: 4 May 2018
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Abstract

Support Vector Regression (SVR), which converts the original low-dimensional problem to a high-dimensional kernel space linear problem by introducing kernel functions, has been successfully applied in system modeling. Regarding the classical SVR algorithm, the value of the features has been taken into account, while its contribution to the model output is omitted. Therefore, the construction of the kernel space may not be reasonable. In the paper, a Feature-Weighted SVR (FW-SVR) is presented. The range of the feature is matched with its contribution by properly assigning the weight of the input features in data pre-processing. FW-SVR optimizes the distribution of the sample points in the kernel space to make the minimizing of the structural risk more reasonable. Four synthetic datasets and seven real datasets are applied. A superior generalization ability is obtained by the proposed method. View Full-Text
Keywords: support vector regression; feature-weighted; contribution; data pre-processing support vector regression; feature-weighted; contribution; data pre-processing
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Xie, M.; Wang, D.; Xie, L. A Feature-Weighted SVR Method Based on Kernel Space Feature. Algorithms 2018, 11, 62.

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