ReviewJournal of biomedical informatics2018
Relief-based feature selection: Introduction and review.
Review in Journal of biomedical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 226 papers, 1 of them a synthesis that pooled it.
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.
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.
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Who cites it
226 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.Scientific reports · 2023Pooled it
- Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer.European journal of nuclear medicine and molecular imaging · 2022Trial
- A multi-strategy integrated improved nutcracker optimization algorithm and its applications in water conservancy engineering.iScience · 2026Article
- Feature Selection Analysis and Robustness to Missing Data for Sensor-Based Prognostic Modeling of Alzheimer's Disease Progression.Sensors (Basel, Switzerland) · 2026Article
- Effect of Emotional States on EEG-Based Biometric Identification: A Comparative Study of Classifiers.Bioengineering (Basel, Switzerland) · 2026Article
- Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.NPJ digital medicine · 2026Article
- A novel and accurate EEG emotion classification model based on multiple attention local binary patterns.BMC medicine · 2026Article
- A fault diagnosis method for complex systems based on hierarchical belief rule base with One-vs-Rest strategy.Scientific reports · 2026Article
- A feature selection-based oblique hyperplane for oblique random survival forests.BMC medical research methodology · 2026Article
- Multidisciplinary prediction of running-related injuries using machine learning.NPJ digital medicine · 2026Article
- Exploration of prognostic prediction models for renal cell carcinoma using diffusion relaxation correlation spectroscopic imaging.BMC medical imaging · 2026Article
- Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson's Disease at Risk of Gait Deterioration.Bioengineering (Basel, Switzerland) · 2026Article
- Automatic diagnosis of age-related macular degeneration using machine learning and image processing techniques.Scientific reports · 2026Article
- Turning heterogeneity of statistical epistasis networks to an advantage.Briefings in bioinformatics · 2026Article
- Computational Pipeline in Neuroradiomics.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Discrete wavelet transform-driven optimized deep learning-based framework for dyslexia detection using EEG signals.Frontiers in neuroinformatics · 2026Article
- Predicting grain growth kinetic in steels using machine learning and XAI for mechanical properties.PloS one · 2026Article
- Uncovering the molecular landscape of young-onset diffuse gastric cancer: A relieff-based feature selection analysis on RNA-Seq data.PloS one · 2026Article
- Unlocking precision diagnostics: A multimodal framework integrating metabolomics with advanced machine learning techniques.PloS one · 2026Article
- Feature fusion and WOA-GWO optimization for Alzheimer's disease detection with sparse EEG channels.Frontiers in computational neuroscience · 2026Article
166 more citing papers are in PubMed but not listed here.
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
Abstract
Feature selection plays a critical role in biomedical data mining, driven by increasing feature dimensionality in target problems and growing interest in advanced but computationally expensive methodologies able to model complex associations. Specifically, there is a need for feature selection methods that are computationally efficient, yet sensitive to complex patterns of association, e.g. interactions, so that informative features are not mistakenly eliminated prior to downstream modeling. This paper focuses on Relief-based algorithms (RBAs), a unique family of filter-style feature selection algorithms that have gained appeal by striking an effective balance between these objectives while flexibly adapting to various data characteristics, e.g. classification vs. regression. First, this work broadly examines types of feature selection and defines RBAs within that context. Next, we introduce the original Relief algorithm and associated concepts, emphasizing the intuition behind how it works, how feature weights generated by the algorithm can be interpreted, and why it is sensitive to feature interactions without evaluating combinations of features. Lastly, we include an expansive review of RBA methodological research beyond Relief and its popular descendant, ReliefF. In particular, we characterize branches of RBA research, and provide comparative summaries of RBA algorithms including contributions, strategies, functionality, time complexity, adaptation to key data characteristics, and software availability.
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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.