Feature selection for high‐dimensional regression via sparse LSSVR based on Lp ‐norm
Sign Up to like & getrecommendations! Published in 2021 at "International Journal of Intelligent Systems"
DOI: 10.1002/int.22334
Abstract: When solving many regression problems, there exist a large number of input features. However, not all features are relevant for current regression, and sometimes, including irrelevant features may deteriorate the learning performance. Therefore, it is… read more here.
Keywords: high dimensional; feature selection; regression; lssvr ... See more keywords
Evaluation of mechanical properties of concretes containing coarse recycled concrete aggregates using multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) models
Sign Up to like & getrecommendations! Published in 2018 at "Neural Computing and Applications"
DOI: 10.1007/s00521-018-3630-y
Abstract: This paper investigates the application of three artificial intelligence methods, including multivariate adaptive regression splines (MARS), M5 model tree (M5Tree), and least squares support vector regression (LSSVR) for the prediction of the mechanical behavior of… read more here.
Keywords: strength; regression splines; regression; adaptive regression ... See more keywords
Machine Learning Models Coupled with Variational Mode Decomposition: A New Approach for Modeling Daily Rainfall-Runoff
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DOI: 10.3390/atmos9070251
Abstract: Accurate modeling for nonlinear and nonstationary rainfall-runoff processes is essential for performing hydrologic practices effectively. This paper proposes two hybrid machine learning models (MLMs) coupled with variational mode decomposition (VMD) to enhance the accuracy for… read more here.
Keywords: machine; daily rainfall; lssvr; vmd ... See more keywords