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Comparing the performance of Ridge Regression and Lasso techniques for modelling daily maximum temperatures in Utraradit Province of Thailand
Daily maximum temperature is one of the important climate components for every country. There are many explanatory variables that may affect a...
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Towards the Analysis of Regularized Denoising Autoencoder for Biosignal Processing: Lasso Versus Ridge Norms
The use of Internet of Things (IoT) that integrate smart bio sensor devices to the internet and shows individual health in real time. Healthcare...
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Screening marker genes of type 2 diabetes mellitus in mouse lacrimal gland by LASSO regression
Type 2 diabetes mellitus (T2DM) is characterized by insulin resistance and a relative deficiency of insulin. This study aims to screen T2DM-related...
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Adaptive group Lasso neural network models for functions of few variables and time-dependent data
Learning nonlinear functions from time-varying measurements is always difficult due to the high correlation among observations. This task is more...
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Improving hospital quality risk-adjustment models using interactions identified by hierarchical group lasso regularisation
BackgroundRisk-adjustment (RA) models are used to account for severity of illness in comparing patient outcomes across hospitals. Researchers specify...
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LASSO for streaming data with adaptative filtering
Streaming data is ubiquitous in modern machine learning, and so the development of scalable algorithms to analyze this sort of information is a topic...
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Optimizing Variable Selection in Multi-Omics Datasets: A Focus on Exclusive Lasso
Multi-omics datasets pose significant challenges due to their structured nature, where highly correlated variables are grouped within a complex,...
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A dual symmetric Gauss-Seidel technique-based proximal ADMM for robust fused lasso estimation
Robust fused lasso (RFlasso) estimation plays an important role in regression analysis because it can deal with variable selection problems more...
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Monitoring of group-structured high-dimensional processes via sparse group LASSO
In a general high-dimensional process, a large number of process parameters or quality characteristics is found to be featured through their...
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Group Lasso based redundancy-controlled feature selection for fuzzy neural network
If there are a lot of inputs, the readability of the “If-then” fuzzy rule is reduced, and the complexity of the fuzzy neural network structure will...
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Experimental investigations on the applicability of LASSO based sparse recovery for compressively sensed OFDM channel estimation
Owing to its success in 4G, Orthogonal Frequency Division Multiplexing (OFDM) made its contenting presence in 5G communications. To accommodate...
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Genome mining of Bacillus licheniformis MCC2514 for the identification of lasso peptide biosynthetic gene cluster and its characterization
The probiotic strain Bacillus licheniformis MCC2514 has been shown to produce a strong antibacterial peptide and the whole genome sequence of this...
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LASSO regularization within the LocalGLMnet architecture
Deep learning models have been very successful in the application of machine learning methods, often out-performing classical statistical models such...
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LASSO and attention-TCN: a concurrent method for indoor particulate matter prediction
Long time exposure to indoor air pollution environments can increase the risk of cardiovascular and respiratory system damage. Most previous studies...
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A practical recurrence risk model based on Lasso-Cox regression for gastric cancer
IntroductionGastric cancer remains huge cancer threat worldwide. Detecting the recurrence of gastric cancer after treatment is especially important...
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Spatio-temporal clustering analysis using generalized lasso with an application to reveal the spread of Covid-19 cases in Japan
This study addressed the issue of determining multiple potential clusters with regularization approaches for the purpose of spatio-temporal...
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Comparison of LASSO and random forest models for predicting the risk of premature coronary artery disease
PurposeWith the change of lifestyle, the occurrence of coronary artery disease presents a younger trend, increasing the medical and economic burden...
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Exploring the factors that influence academic stress among elementary school students using a LASSO penalty regression model
The main objective of the study was to explore the main predictors that influence academic stress among fourth-grade elementary school students using...
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Sparse and debiased lasso estimation and inference for high-dimensional composite quantile regression with distributed data
We consider the data are inherently distributed and focus on statistical learning in the presence of heavy-tailed and/or asymmetric errors. The...
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Generalized fused Lasso for grouped data in generalized linear models
Generalized fused Lasso (GFL) is a powerful method based on adjacent relationships or the network structure of data. It is used in a number of...
