ArticleGenes2018
Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE.
Article in Genes, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 49 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
49 citing papers in PubMed.
- Article
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- Quantification of head and neck cancer patients' anatomical changes during radiotherapy: Toward the prediction of replanning need.Journal of applied clinical medical physics · 2026Article
- Unlocking precision diagnostics: A multimodal framework integrating metabolomics with advanced machine learning techniques.PloS one · 2026Article
- Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM.Annals of medicine · 2025Article
- Behavioral Analysis of Postgraduate Education Satisfaction: Unveiling Key Influencing Factors with Bayesian Networks and Feature Importance.Behavioral sciences (Basel, Switzerland) · 2025Article
- Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.Scientific reports · 2025Article
- Impact of Climate Change on the Narrow Endemic HerbEcology and evolution · 2025Article
- Role of fatty acid metabolism-related genes in periodontitis based on machine learning and bioinformatics analysis.Hua xi kou qiang yi xue za zhi = Huaxi kouqiang yixue zazhi = West China journal of stomatology · 2024Article
- A novel MissForest-based missing values imputation approach with recursive feature elimination in medical applications.BMC medical research methodology · 2024Article
- AI-Powered Mining of Highly Customized and Superior ESIPT-Based Fluorescent Probes.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- Follicular Fluid Proteomic Analysis to Identify Predictive Markers of Normal Embryonic Development.International journal of molecular sciences · 2024Article
- Estimation of the rice aboveground biomass based on the first derivative spectrum and Boruta algorithm.Frontiers in plant science · 2024Article
- Stacking with Recursive Feature Elimination-Isolation Forest for classification of diabetes mellitus.PloS one · 2024Article
- A comprehensive analysis and performance evaluation for osteoporosis prediction models.PeerJ. Computer science · 2024Article
- Blood-Based Transcriptomic Biomarkers Are Predictive of Neurodegeneration Rather Than Alzheimer's Disease.International journal of molecular sciences · 2023Article
- AUD-DSS: a decision support system for early detection of patients with alcohol use disorder.BMC bioinformatics · 2023Article
- Socio-Environmental Determinants of Mental and Behavioral Disorders in Youth: A Machine Learning Approach.GeoHealth · 2023Article
- Machine learning model to predict obesity using gut metabolite and brain microstructure data.Scientific reports · 2023Article
- Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Feature selection, which identifies a set of most informative features from the original feature space, has been widely used to simplify the predictor. Recursive feature elimination (RFE), as one of the most popular feature selection approaches, is effective in data dimension reduction and efficiency increase. A ranking of features, as well as candidate subsets with the corresponding accuracy, is produced through RFE. The subset with highest accuracy (HA) or a preset number of features (PreNum) are often used as the final subset. However, this may lead to a large number of features being selected, or if there is no prior knowledge about this preset number, it is often ambiguous and subjective regarding final subset selection. A proper decision variant is in high demand to automatically determine the optimal subset. In this study, we conduct pioneering work to explore the decision variant after obtaining a list of candidate subsets from RFE. We provide a detailed analysis and comparison of several decision variants to automatically select the optimal feature subset. Random forest (RF)-recursive feature elimination (RF-RFE) algorithm and a voting strategy are introduced. We validated the variants on two totally different molecular biology datasets, one for a toxicogenomic study and the other one for protein sequence analysis. The study provides an automated way to determine the optimal feature subset when using RF-RFE.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.