Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels

Robert Aduviri, Daniel Matos, Edwin Villanueva

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

7 Citas (Scopus)


Feature selection is an important step in gene expression data analysis. However, many feature selection methods exist and a costly experimentation is usually needed to determine the most suitable one for a given problem. This paper presents the application of gradient boosting and neural network techniques for the construction of metamodels that can recommend rankings of {feature selection - classification} algorithm pairs for new gene expression classification problems. Results in a corpus of 60 public data sets show the superiority of these techniques in producing more useful rankings in relation to classical metamodels.
Idioma originalEspañol
Título de la publicación alojadaProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Número de páginas3
EstadoPublicada - 21 ene. 2019
Publicado de forma externa

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