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RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers

Nicola Lazzarini; Jaume Bacardit


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{
  "description": "<p>RGIFE is a feature reduction heuristic for the identification of small panels of highly predictive biomarkers. The heuristic is based on an <strong>iterative reduction paradigm</strong>: first train a classifier, then rank the attributes based on their importance and finally remove attributes in block. RGIFE is designed to work with large-scale datasets and identifies reduced set of attributes with high classification power.</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Newcastle University", 
      "@type": "Person", 
      "name": "Nicola Lazzarini"
    }, 
    {
      "affiliation": "Newcastle University", 
      "@type": "Person", 
      "name": "Jaume Bacardit"
    }
  ], 
  "url": "https://zenodo.org/record/193015", 
  "datePublished": "2016-12-06", 
  "keywords": [
    "biomarkers", 
    "feature selection", 
    "omics", 
    "classification", 
    "machine learning"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.193015", 
  "@id": "https://doi.org/10.5281/zenodo.193015", 
  "@type": "SoftwareSourceCode", 
  "name": "RGIFE: a ranked guided iterative feature elimination heuristic for the identification of biomarkers"
}
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