ArticleProceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications2018
Recursive Feature Elimination by Sensitivity Testing.
Article in Proceedings of the ... International Conference on Machine Learning and Applications. International Conference on Machine Learning and Applications, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 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
33 citing papers in PubMed.
- Development and validation of an interpretable Random Forest model for predicting recurrence after endoscopic submucosal dissection in superficial oesophageal squamous cell carcinoma.Annals of medicine · 2026Article
- Machine learning model for predicting rebleeding risk after endoscopic variceal ligation in esophageal variceal bleeding.Scientific reports · 2026Article
- Development and preliminary validation of an explainable machine learning model for early ROSC detection during chest compression in a swine model of cardiac arrest.Resuscitation plus · 2026Article
- Building and Validating an Explainable Machine Learning Model for Predicting Health-Promoting Behaviors in Older Adults: A Multicenter Study.Prevention science : the official journal of the Society for Prevention Research · 2026Article
- Prediction of Adherence to an Online Wellness Program for People with Mobility Limitations: A Machine Learning Approach.Healthcare (Basel, Switzerland) · 2026Article
- An automated decision making framework for modern vehicles COScientific reports · 2026Article
- Artificial intelligence to investigate metabolomics data for precision medicine.Metabolomics : Official journal of the Metabolomic Society · 2026Review
- Video-based machine learning models for predicting deep brain stimulation outcomes in Parkinson's disease patients.NPJ Parkinson's disease · 2026Article
- Explainable machine learning for osteoporosis detection in patients with osteopenia: model development and validation using routine clinical data from an Asian cohort.Frontiers in endocrinology · 2026Article
- Magnetoencephalographic Spectral Abnormalities and Explainable Machine Learning of Neuropathic-Like Pain in Knee Osteoarthritis.Journal of pain research · 2026Article
- Interpretable machine learning model based on multimodal ultrasound for bedside diagnosis of acute exacerbations in COPD.Respiratory research · 2025Article
- Identification of protein biomarkers to differentiate between gram-negative and gram-positive infections in adults suspected of sepsis.BMC infectious diseases · 2025Article
- Metagenomic next-generation sequencing unraveled the characteristic of lung microbiota in patients with checkpoint inhibitor pneumonitis: results from a prospective cohort study.Journal for immunotherapy of cancer · 2025Observational
- Development and validation of a predictive model for in-hospital mortality in patients with coronary heart disease and renal insufficiency.International journal of cardiology. Cardiovascular risk and prevention · 2025Article
- Skin Microbiome alterations in heroin users revealed by full-length 16S rRNA sequencing.BMC microbiology · 2025Article
- An explainable machine learning model for predicting the risk of distant metastasis in intrahepatic cholangiocarcinoma: a population-based cohort study.Discover oncology · 2025Article
- Prediction of tablet disintegration time based on formulations properties via artificial intelligence by comparing machine learning models and validation.Scientific reports · 2025Article
- Utilization of machine learning algorithm in the prediction of rehospitalization during one-year post traumatic spinal cord injury.Spinal cord · 2025Article
- Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.Scientific reports · 2025Article
- Intelligence analysis of drug nanoparticles delivery efficiency to cancer tumor sites using machine learning models.Scientific reports · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
There is great interest in methods to improve human insight into trained non-linear models. Leading approaches include producing a ranking of the most relevant features, a non-trivial task for non-linear models. We show theoretically and empirically the benefit of a novel version of recursive feature elimination (RFE) as often used with SVMs; the key idea is a simple twist on the kinds of sensitivity testing employed in computational learning theory with membership queries (e.g., [1]). With membership queries, one can check whether changing the value of a feature in an example changes the label. In the real-world, we usually cannot get answers to such queries, so our approach instead makes these queries to a trained (imperfect) non-linear model. Because SVMs are widely used in bioinformatics, our empirical results use a real-world cancer genomics problem; because ground truth is not known for this task, we discuss the
Identifiers
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.