2024-03-29T00:10:25Zhttps://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
When is resampling beneficial for feature selection with imbalanced wide data?
Ramos Pérez, Ismael
Arnaiz González, Álvar
Rodríguez Diez, Juan José
García Osorio, César
Feature selection
Wide data
High dimensional data
Very low sample size
Unbalanced
Machine learning
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.
2021-11-19T10:18:52Z
2021-11-19T10:18:52Z
2022-02
info:eu-repo/semantics/article
0957-4174
http://hdl.handle.net/10259/6192
10.1016/j.eswa.2021.116015
eng
Expert Systems with Applications. 2022, V. 188, 116015
https://doi.org/10.1016/j.eswa.2021.116015
info:eu-repo/grantAgreement/Fundación Bancaria Caixa d'Estalvis i Pensions de Barcelona//LCF%2FPR%2FPR18%2F51130007
info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0
http://creativecommons.org/licenses/by-nc-nd/4.0/
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Elsevier