@inproceedings{67260b9de00f412791265eea73d82888,
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,
day = "21",
doi = "10.1109/BIBM.2018.8621397",
language = "English",
series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2726--2728",
editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",
booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",
}