A cost-sensitive constrained Lasso

DOI10.1007/s11634-020-00389-5OpenAlexW3011249011MaRDI QIDQ2036145

Rafael Blanquero, Emilio Carrizosa, Pepa Ramírez-Cobo, M. Remedios Sillero-Denamiel

Publication date: 28 June 2021

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://idus.us.es/handle//11441/107464

zbMATH Keywords

heterogeneityperformance constraintsLassosparse solutionssample average approximationcost-sensitive learning

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics (62P99)

Related Items

Variable selection for Naïve Bayes classification ⋮ On sparse optimal regression trees ⋮ Robust optimal classification trees under noisy labels ⋮ On optimal regression trees to detect critical intervals for multivariate functional data ⋮ On sparse ensemble methods: an application to short-term predictions of the evolution of COVID-19 ⋮ Constrained Naïve Bayes with application to unbalanced data classification

Uses Software

• UCI-ml
• Gurobi
• ConstrainedLasso

Cites Work

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• The Adaptive Lasso and Its Oracle Properties
• Statistics for high-dimensional data. Methods, theory and applications.
• The solution path of the generalized lasso
• The dual and degrees of freedom of linearly constrained generalized Lasso
• Near-Bayesian support vector machines for imbalanced data classification with equal or unequal misclassification costs
• A data-driven software tool for enabling cooperative information sharing among police departments
• Least angle regression. (With discussion)
• Multi-group support vector machines with measurement costs: A biobjective approach
• Penalized and Constrained Optimization: An Application to High-Dimensional Website Advertising
• Algorithms for Fitting the Constrained Lasso
• Combining Minsum And Minmax: A Goal Programming Approach
• Lectures on Stochastic Programming
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Sparsity and Smoothness Via the Fused Lasso
• Regularization and Variable Selection Via the Elastic Net
• Imbalanced Learning
• Regression modelling on stratified data with the lasso
• Model Selection and Estimation in Regression with Grouped Variables
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• The elements of statistical learning. Data mining, inference, and prediction