ArticleEntropy (Basel, Switzerland)2023
On the Relationship between Feature Selection Metrics and Accuracy.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- A Chaos-Enhanced Binary Newton-Raphson Optimizer for High-Dimensional Sensor Data Feature Selection.Sensors (Basel, Switzerland) · 2026Article
- Machine learning approach for predicting the severity risk of obstructive sleep apnea syndrome.Frontiers in big data · 2026Article
- Information-Theoretic Measures of Metacognition: Bounds and Relation to Group Performance.Open mind : discoveries in cognitive science · 2025Article
- Statistics in Service of Metascience: Measuring Replication Distance with Reproducibility Rate.Entropy (Basel, Switzerland) · 2024Article
- Chain hybrid feature selection algorithm based on improved Grey Wolf Optimization algorithm.PloS one · 2024Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Feature selection metrics are commonly used in the machine learning pipeline to rank and select features before creating a predictive model. While many different metrics have been proposed for feature selection, final models are often evaluated by accuracy. In this paper, we consider the relationship between common feature selection metrics and accuracy. In particular, we focus on
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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.