An Efficient Feature Weighting Method for Support Vector Regression. (2nd March 2021)

• Record Type: Journal Article
• Authors: Xie, Minghua; Xie, Lili; Zhu, Peidong
• Other Names: Pandey K. M. Academic Editor.
• Is Part Of: Mathematical problems in engineering. Volume 2021(2021)
• Publication Date: 2021-03-02
• Subjects: Engineering mathematics -- Periodicals; 510.2462
• DOI: 10.1155/2021/6675218
• Languages: English
• ISSNs: 1024-123X

Abstract

Abstract : Support vector regression (SVR) is a powerful kernel-based method which has been successfully applied in regression problems. Regarding the feature-weighted SVR algorithms, its contribution to model output has been taken into account. However, the performance of the model is subject to the feature weights and the time consumption on training. In the paper, an efficient feature-weighted SVR is proposed. Firstly, the value constraint of each weight is obtained according to the maximal information coefficient which reveals the relationship between each input feature and output. Then, the constrained particle swarm optimization (PSO) algorithm is employed to optimize the feature weights and the hyperparameters simultaneously. Finally, the optimal weights are used to modify the kernel function. Simulation experiments were conducted on four synthetic datasets and seven real datasets by using the proposed model, classical SVR, and some state-of-the-art feature-weighted SVR models. The results show that the proposed method has the superior generalization ability within acceptable time.