ArticleBMC bioinformatics2020
GARS: Genetic Algorithm for the identification of a Robust Subset of features in high-dimensional datasets.
Article in BMC bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
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- Whole-Blood Transcriptome Unveils Altered Immune Response in Acute Myocardial Infarction Patients With Aortic Valve Sclerosis.Arteriosclerosis, thrombosis, and vascular biology · 2024Article
- Prediction of Breast Cancer using Machine Learning Approaches.Journal of biomedical physics & engineering · 2022Article
- BiGAMi: Bi-Objective Genetic Algorithm Fitness Function for Feature Selection on Microbiome Datasets.Methods and protocols · 2022Article
- BOSO: A novel feature selection algorithm for linear regression with high-dimensional data.PLoS computational biology · 2022Article
- Article
- Framework for classification of cancer gene expression data using Bayesian hyper-parameter optimization.Medical & biological engineering & computing · 2021Article
- Multiomic Approaches to Uncover the Complexities of Dystrophin-Associated Cardiomyopathy.International journal of molecular sciences · 2021Review
- Putative Circulating MicroRNAs Are Able to Identify Patients with Mitral Valve Prolapse and Severe Regurgitation.International journal of molecular sciences · 2021Article
- A systematic review of emerging feature selection optimization methods for optimal text classification: the present state and prospective opportunities.Neural computing & applications · 2021Review
- Advanced feature selection to study the internationalization strategy of enterprises.PeerJ. Computer science · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundFeature selection is a crucial step in machine learning analysis. Currently, many feature selection approaches do not ensure satisfying results, in terms of accuracy and computational time, when the amount of data is huge, such as in 'Omics' datasets.
resultsHere, we propose an innovative implementation of a genetic algorithm, called GARS, for fast and accurate identification of informative features in multi-class and high-dimensional datasets. In all simulations, GARS outperformed two standard filter-based and two 'wrapper' and one embedded' selection methods, showing high classification accuracies in a reasonable computational time.
conclusionsGARS proved to be a suitable tool for performing feature selection on high-dimensional data. Therefore, GARS could be adopted when standard feature selection approaches do not provide satisfactory results or when there is a huge amount of data to be analyzed.
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