ReviewPeerJ2023
Review of feature selection approaches based on grouping of features.
Review in PeerJ, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- High-Dimensional Feature Selection Using Improved Hybrid Breeding Optimization Algorithm with Feature Grouping.Biomimetics (Basel, Switzerland) · 2026Article
- An explainable AI-driven hybrid feature selection approach for coronary artery disease diagnosis.Scientific reports · 2026Article
- Determinants of oral functions and oral frailty in older community-dwelling individuals: a comprehensive analysis.The journals of gerontology. Series A, Biological sciences and medical sciences · 2026Article
- Explanation Beyond Individual Features: Instance-wise Feature Grouping for EHR Predictive Analytics.Journal of healthcare informatics research · 2026Article
- TAGINE: fast taxonomy-based feature engineering for microbiome analysis.Bioinformatics advances · 2026Article
- Stacked Ensemble Learning for Classification of Parkinson's Disease Using Telemonitoring Vocal Features.Diagnostics (Basel, Switzerland) · 2025Article
- RCE-IFE: recursive cluster elimination with intra-cluster feature elimination.PeerJ. Computer science · 2025Article
- Topic selection for text classification using ensemble topic modeling with grouping, scoring, and modeling approach.Scientific reports · 2024Article
- Strategies for overcoming data scarcity, imbalance, and feature selection challenges in machine learning models for predictive maintenance.Scientific reports · 2024Article
- microBiomeGSM: the identification of taxonomic biomarkers from metagenomic data using grouping, scoring and modeling (G-S-M) approach.Frontiers in microbiology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With the rapid development in technology, large amounts of high-dimensional data have been generated. This high dimensionality including redundancy and irrelevancy poses a great challenge in data analysis and decision making. Feature selection (FS) is an effective way to reduce dimensionality by eliminating redundant and irrelevant data. Most traditional FS approaches score and rank each feature individually; and then perform FS either by eliminating lower ranked features or by retaining highly-ranked features. In this review, we discuss an emerging approach to FS that is based on initially grouping features, then scoring groups of features rather than scoring individual features. Despite the presence of reviews on clustering and FS algorithms, to the best of our knowledge, this is the first review focusing on FS techniques based on grouping. The typical idea behind FS through grouping is to generate groups of similar features with dissimilarity between groups, then select representative features from each cluster. Approaches under supervised, unsupervised, semi supervised and integrative frameworks are explored. The comparison of experimental results indicates the effectiveness of sequential, optimization-based (
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What OpenQuestion holds
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.