Coupled Least Squares Support Vector Ensemble Machines

Abstract

The least squares support vector method is a popular data-driven modeling method which shows better performance and has been successfully applied in a wide range of applications. In this paper, we propose a novel coupled least squares support vector ensemble machine (C-LSSVEM). The proposed coupling ensemble helps improve robustness and produce good classification performance than the single model approach. The proposed C-LSSVEM can choose appropriate kernel types and their parameters in a good coupling strategy with a set of classifiers being trained simultaneously. The proposed method can further minimize the total loss of ensembles in kernel space. Thus, we form an ensemble regressor by co-optimizing and weighing base regressors. Experiments conducted on several datasets such as artificial datasets, UCI classification datasets, UCI regression datasets, handwritten digits datasets and NWPU-RESISC45 datasets, indicate that C-LSSVEM performs better in achieving the minimal regression loss and the best classification accuracy relative to selected state-of-the-art regression and classification techniques.

Air temperature prediction using different machine learning models

“…A variety of kernel functions (i.e. linear, polynomial, radial basis, and spline) have been available to solve the regression or mapping problems [23][24][25]. The current research utilized radial basis function (RBF) as kernel function for mapping the data into a high dimensional feature space and expressed by (3) as:…”