Feature Selection via Genetic Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2002-08-21

AUTHORS

Sancho Salcedo-Sanz , Mario Prado-Cumplido , Fernando Pérez-Cruz , Carlos Bousoño-Calzón

ABSTRACT

In this paper we present a novel Genetic Algorithm (GA) for feature selection in machine learning problems. We introduce a novel genetic operator which fixes the number of selected features. This operator, we will refer to it as m-features operator, reduces the size of the search space and improves the GA performance and convergence. Simulations on synthetic and real problems have shown very good performance of the m-features operator, improving the performance of other existing approaches over the feature selection problem. More... »

PAGES

547-552

References to SciGraph publications

  • 1997-09. The Stanford Digital Library metadata architecture in INTERNATIONAL JOURNAL ON DIGITAL LIBRARIES
  • Book

    TITLE

    Artificial Neural Networks — ICANN 2002

    ISBN

    978-3-540-44074-1
    978-3-540-46084-8

    Author Affiliations

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/3-540-46084-5_89

    DOI

    http://dx.doi.org/10.1007/3-540-46084-5_89

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1046671184


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