Feature selection under budget constraint in medical applications: analysis of penalized empirical risk minimization methods
Published 2023 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Feature selection under budget constraint in medical applications: analysis of penalized empirical risk minimization methods
Authors
Keywords
-
Journal
APPLIED INTELLIGENCE
Volume -, Issue -, Pages -
Publisher
Springer Science and Business Media LLC
Online
2023-11-06
DOI
10.1007/s10489-023-05063-9
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- An enhanced grey wolf optimizer with fusion strategies for identifying the parameters of photovoltaic models
- (2022) Jinkun Luo et al. INTEGRATED COMPUTER-AIDED ENGINEERING
- Application of Deep Learning Techniques in Diagnosis of Covid-19 (Coronavirus): A Systematic Review
- (2022) Yogesh H. Bhosale et al. NEURAL PROCESSING LETTERS
- MLFS-CCDE: multi-objective large-scale feature selection by cooperative coevolutionary differential evolution
- (2021) Haoran Li et al. Memetic Computing
- Development and Validation of a LASSO Prediction Model for Better Identification of Ischemic Stroke: A Case-Control Study in China
- (2021) Zirui Meng et al. Frontiers in Aging Neuroscience
- Controlling Costs: Feature Selection on a Budget
- (2021) Guo Yu et al. Stat
- Re-epithelialization and immune cell behaviour in an ex vivo human skin model
- (2020) Ana Rakita et al. Scientific Reports
- A review of feature selection methods in medical applications
- (2019) Beatriz Remeseiro et al. COMPUTERS IN BIOLOGY AND MEDICINE
- Comparison of variable selection methods for clinical predictive modeling
- (2018) L. Nelson Sanchez-Pinto et al. INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
- The utility of LASSO-based models for real time forecasts of endemic infectious diseases: A cross country comparison
- (2018) Yirong Chen et al. JOURNAL OF BIOMEDICAL INFORMATICS
- Panning for gold: ‘model-X’ knockoffs for high dimensional controlled variable selection
- (2018) Emmanuel Candès et al. JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
- Cost-sensitive classifier chains: selecting low-cost features in multi-label classification
- (2018) Paweł Teisseyre et al. PATTERN RECOGNITION
- Lasso Regression for the Prediction of Intermediate Outcomes Related to Cardiovascular Disease Prevention Using the TRANSIT Quality Indicators
- (2018) Cynthia Khanji et al. MEDICAL CARE
- Cost-sensitive feature selection using random forest: Selecting low-cost subsets of informative features
- (2016) Qifeng Zhou et al. KNOWLEDGE-BASED SYSTEMS
- Controlling the false discovery rate via knockoffs
- (2015) Rina Foygel Barber et al. ANNALS OF STATISTICS
- Review and evaluation of penalised regression methods for risk prediction in low-dimensional data with few events
- (2015) Menelaos Pavlou et al. STATISTICS IN MEDICINE
- A framework for cost-based feature selection
- (2014) V. Bolón-Canedo et al. PATTERN RECOGNITION
- Multiparameter Intelligent Monitoring in Intensive Care II: A public-access intensive care unit database*
- (2011) Mohammed Saeed et al. CRITICAL CARE MEDICINE
- To Explain or to Predict?
- (2011) Galit Shmueli STATISTICAL SCIENCE
- Nearly unbiased variable selection under minimax concave penalty
- (2010) Cun-Hui Zhang ANNALS OF STATISTICS
- Sparse regression techniques in low-dimensional survival data settings
- (2009) Christine Porzelius et al. STATISTICS AND COMPUTING
- Cancer risks from diagnostic radiology
- (2008) E J HALL et al. BRITISH JOURNAL OF RADIOLOGY
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationPublish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn More