2026-05-01T09:29:54Zhttps://riubu.ubu.es/oai/request

oai: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

Ramos Pérez, Ismael Arnaiz González, Álvar Rodríguez Diez, Juan José 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 http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess 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