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High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection

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Predictive Society and Data Analytics Lab, Faculty of Information Technology and Communication Sciences, Tampere University, 33720 Tampere, Finland
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Institute of Biosciences and Medical Technology, 33520 Tampere, Finland
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Institute for Intelligent Production, Faculty for Management, University of Applied Sciences Upper Austria, 4400 Steyr Campus, Austria
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Department of Mechatronics and Biomedical Computer Science, UMIT, 6060 Hall in Tyrol, Austria
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College of Computer and Control Engineering, Nankai University, Tianjin 300071, China
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Author to whom correspondence should be addressed.
Mach. Learn. Knowl. Extr. 2019, 1(1), 359-383; https://doi.org/10.3390/make1010021
Received: 9 December 2018 / Revised: 31 December 2018 / Accepted: 11 January 2019 / Published: 14 January 2019
(This article belongs to the Section Learning)
Regression models are a form of supervised learning methods that are important for machine learning, statistics, and general data science. Despite the fact that classical ordinary least squares (OLS) regression models have been known for a long time, in recent years there are many new developments that extend this model significantly. Above all, the least absolute shrinkage and selection operator (LASSO) model gained considerable interest. In this paper, we review general regression models with a focus on the LASSO and extensions thereof, including the adaptive LASSO, elastic net, and group LASSO. We discuss the regularization terms responsible for inducing coefficient shrinkage and variable selection leading to improved performance metrics of these regression models. This makes these modern, computational regression models valuable tools for analyzing high-dimensional problems. View Full-Text
Keywords: machine learning; statistics; regression models; LASSO; regularization; high-dimensional data; data science; shrinkage; feature selection machine learning; statistics; regression models; LASSO; regularization; high-dimensional data; data science; shrinkage; feature selection
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Emmert-Streib, F.; Dehmer, M. High-Dimensional LASSO-Based Computational Regression Models: Regularization, Shrinkage, and Selection. Mach. Learn. Knowl. Extr. 2019, 1, 359-383.

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