Artículo
Memetic micro-genetic algorithms for cancer data classification
Fecha de publicación:
01/2023
Editorial:
Elsevier
Revista:
Intelligent Systems with Applications
ISSN:
2667-3053
Idioma:
Inglés
Tipo de recurso:
Artículo publicado
Clasificación temática:
Resumen
Fast and precise medical diagnosis of human cancer is crucial for treatment decisions. Gene selection consists of identifying a set of informative genes from microarray data to allow high predictive accuracy in human cancer classification. This task is a combinatorial search problem, and optimisation methods can be applied for its resolution. In this paper, two memetic micro-genetic algorithms (MμV1 and MμV2) with different hybridisation approaches are proposed for feature selection of cancer microarray data. Seven gene expression datasets are used for experimentation. The comparison with stochastic state-of-the-art optimisation techniques concludes that problem-dependent local search methods combined with micro-genetic algorithms improve feature selection of cancer microarray data.
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Articulos (ICIC)
Articulos de INSTITUTO DE CS. E INGENIERIA DE LA COMPUTACION
Articulos de INSTITUTO DE CS. E INGENIERIA DE LA COMPUTACION
Articulos(CCT - MENDOZA)
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Articulos de CTRO.CIENTIFICO TECNOL.CONICET - MENDOZA
Citación
Rojas, Matias Gabriel; Olivera, Ana Carolina; Carballido, Jessica Andrea; Vidal, Pablo Javier; Memetic micro-genetic algorithms for cancer data classification; Elsevier; Intelligent Systems with Applications; 17; 1-2023; 1-16
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