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

Robert Aduviri; Daniel Matos; Edwin Villanueva

doi:10.1109/BIBM.2018.8621397

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

Robert Aduviri
, Daniel Matos
, Edwin Villanueva

Sección de Ingeniería Informática

Pontifical Catholic Univ. of Peru

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión 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 original	Inglés
Título de la publicación alojada	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editores	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	2726-2728
Número de páginas	3
ISBN (versión digital)	9781538654880
DOI	https://doi.org/10.1109/BIBM.2018.8621397
Estado	Publicada - 21 ene. 2019
Evento	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Espana Duración: 3 dic. 2018 → 6 dic. 2018

Serie de la publicación

Nombre	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conferencia

Conferencia	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
País/Territorio	Espana
Ciudad	Madrid
Período	3/12/18 → 6/12/18

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ODS 3: Salud y bienestar

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10.1109/BIBM.2018.8621397

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Aduviri, R., Matos, D., & Villanueva, E. (2019). Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels. En H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2726-2728). Artículo 8621397 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621397

Aduviri, Robert ; Matos, Daniel ; Villanueva, Edwin. / Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2726-2728 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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title = "Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels",

abstract = "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.",

keywords = "algorithm recommendation, cancer classification, feature selection, gene expression data, Metalearning",

author = "Robert Aduviri and Daniel Matos and Edwin Villanueva",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

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doi = "10.1109/BIBM.2018.8621397",

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series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

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Aduviri, R, Matos, D & Villanueva, E 2019, Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels. En H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621397, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2726-2728, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Espana, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621397

Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels. / Aduviri, Robert; Matos, Daniel; Villanueva, Edwin.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2726-2728 8621397 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

TY - GEN

T1 - Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels

AU - Aduviri, Robert

AU - Matos, Daniel

AU - Villanueva, Edwin

PY - 2019/1/21

Y1 - 2019/1/21

N2 - 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.

AB - 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.

KW - algorithm recommendation

KW - cancer classification

KW - feature selection

KW - gene expression data

KW - Metalearning

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DO - 10.1109/BIBM.2018.8621397

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EP - 2728

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

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Aduviri R, Matos D, Villanueva E. Feature selection algorithm recommendation for gene expression data through gradient boosting and neural network metamodels. En Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editores, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2726-2728. 8621397. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621397

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

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