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Open AccessArticle

Coupled Least Squares Support Vector Ensemble Machines

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School of Computer Science and Communication Engineering, JiangSu University, Zhenjiang 212013, China
2
School of Computer Science, Datalink Institute, P.O. Box CO 2481 Tema, Ghana
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Author to whom correspondence should be addressed.
Information 2019, 10(6), 195; https://doi.org/10.3390/info10060195
Received: 3 April 2019 / Revised: 30 April 2019 / Accepted: 23 May 2019 / Published: 3 June 2019
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. View Full-Text
Keywords: least squares support vector machines; kernel ensemble; regression; classification least squares support vector machines; kernel ensemble; regression; classification
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Wornyo, D.K.; Shen, X.-J. Coupled Least Squares Support Vector Ensemble Machines. Information 2019, 10, 195.

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