2026-06-21T21:50:00Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/61922022-11-23T11:03:38Zcom_10259_4219com_10259_5086com_10259_2604com_10259_6190com_10259_6189com_10259.4_106col_10259_4220col_10259_6191
Repositorio Institucional de la Universidad de Burgos
author
Ramos Pérez, Ismael
author
Arnaiz González, Álvar
author
Rodríguez Diez, Juan José
author
García Osorio, César
2021-11-19T10:18:52Z
2021-11-19T10:18:52Z
2022-02
0957-4174
http://hdl.handle.net/10259/6192
10.1016/j.eswa.2021.116015
This paper studies the effects that combinations of balancing and feature selection techniques have on wide data (many more attributes than instances) when different classifiers are used. For this, an extensive study is done using 14 datasets, 3 balancing strategies, and 7 feature selection algorithms. The evaluation is carried out using 5 classification algorithms, analyzing the results for different percentages of selected features, and establishing the statistical significance using Bayesian tests.
Some general conclusions of the study are that it is better to use RUS before the feature selection, while ROS and SMOTE offer better results when applied afterwards. Additionally, specific results are also obtained depending on the classifier used, for example, for Gaussian SVM the best performance is obtained when the feature selection is done with SVM-RFE before balancing the data with RUS.
eng
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Feature selection
Wide data
High dimensional data
Very low sample size
Unbalanced
Machine learning
When is resampling beneficial for feature selection with imbalanced wide data?
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/6192/1/Ramos-esa_2022.pdf
File
MD5
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1670522
application/pdf
Ramos-esa_2022.pdf