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)

Resumen

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 originalInglés
Título de la publicación alojadaProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
EditoresHarald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas2726-2728
Número de páginas3
ISBN (versión digital)9781538654880
DOI
EstadoPublicada - 21 ene. 2019
Evento2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Espana
Duración: 3 dic. 20186 dic. 2018

Serie de la publicación

NombreProceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conferencia

Conferencia2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
País/TerritorioEspana
CiudadMadrid
Período3/12/186/12/18

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