ArticleIEEE transactions on pattern analysis and machine intelligence2018
Ensembles of Lasso Screening Rules.
Article in IEEE transactions on pattern analysis and machine intelligence, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Age-specific prevalence and predictors of lifetime suicide attempts using machine learning in Chinese adults: a nationwide multi-centre survey.Epidemiology and psychiatric sciences · 2025Article
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- Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm.Frontiers in oncology · 2024Article
- Developing and validating a machine learning model to predict multidrug-resistantFrontiers in immunology · 2024Article
- Construction of an Early Alert System for Intradialytic Hypotension before Initiating Hemodialysis Based on Machine Learning.Kidney diseases (Basel, Switzerland) · 2023Article
- Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma.Frontiers in immunology · 2021Article
- Fast computation of genome-metagenome interaction effects.Algorithms for molecular biology : AMB · 2020Article
- A Screening Rule forAdvances in neural information processing systems · 2017Article
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5 authors.
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Abstract
In order to solve large-scale lasso problems, screening algorithms have been developed that discard features with zero coefficients based on a computationally efficient screening rule. Most existing screening rules were developed from a spherical constraint and half-space constraints on a dual optimal solution. However, existing rules admit at most two half-space constraints due to the computational cost incurred by the half-spaces, even though additional constraints may be useful to discard more features. In this paper, we present AdaScreen, an adaptive lasso screening rule ensemble, which allows to combine any one sphere with multiple half-space constraints on a dual optimal solution. Thanks to geometrical considerations that lead to a simple closed form solution for AdaScreen, we can incorporate multiple half-space constraints at small computational cost. In our experiments, we show that AdaScreen with multiple half-space constraints simultaneously improves screening performance and speeds up lasso solvers.
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