ArticleComputational and mathematical methods in medicine2017
Probing for Sparse and Fast Variable Selection with Model-Based Boosting.
Article in Computational and mathematical methods in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population.Frontiers in public health · 2024Article
- Regularization approaches in clinical biostatistics: A review of methods and their applications.Statistical methods in medical research · 2023Review
- Evaluating the risk of hypertension in residents in primary care in Shanghai, China with machine learning algorithms.Frontiers in public health · 2022Article
- A statistical boosting framework for polygenic risk scores based on large-scale genotype data.Frontiers in genetics · 2022Article
- Randomized boosting with multivariable base-learners for high-dimensional variable selection and prediction.BMC bioinformatics · 2021Article
- Machine learning-based outcome prediction and novel hypotheses generation for substance use disorder treatment.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Comparison of Prediction Models for Acute Kidney Injury Among Patients with Hepatobiliary Malignancies Based on XGBoost and LASSO-Logistic Algorithms.International journal of general medicine · 2021Article
- Classification models for Invasive Ductal Carcinoma Progression, based on gene expression data-trained supervised machine learning.Scientific reports · 2020Article
- Feature Genes Selection Using Supervised Locally Linear Embedding and Correlation Coefficient for Microarray Classification.Computational and mathematical methods in medicine · 2018Article
- Corrigendum to "Probing for Sparse and Fast Variable Selection with Model-Based Boosting".Computational and mathematical methods in medicine · 2018Article
- An Update on Statistical Boosting in Biomedicine.Computational and mathematical methods in medicine · 2017Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a new variable selection method based on model-based gradient boosting and randomly permuted variables. Model-based boosting is a tool to fit a statistical model while performing variable selection at the same time. A drawback of the fitting lies in the need of multiple model fits on slightly altered data (e.g., cross-validation or bootstrap) to find the optimal number of boosting iterations and prevent overfitting. In our proposed approach, we augment the data set with randomly permuted versions of the true variables, so-called shadow variables, and stop the stepwise fitting as soon as such a variable would be added to the model. This allows variable selection in a single fit of the model without requiring further parameter tuning. We show that our probing approach can compete with state-of-the-art selection methods like stability selection in a high-dimensional classification benchmark and apply it on three gene expression data sets.
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