An Efficient Feature Weighting Method for Support Vector Regression

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.

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“…Support Vector Regression (SVR) is a generalization of Support Vector Machine (SVM) that incorporates regression functions into SVM to solve regression problems [ 39 , 40 ]. As a supervised machine learning algorithm, SVR has a high capability in regression modeling [ 41 , 42 ]. SVR is a kernel-based technique in which the kernel function projects the input data into higher-dimensional feature space to find the hyperplane with the lowest error margin and the best fit to the regression line [ 43 , 44 ].…”