When is resampling beneficial for feature selection with imbalanced wide data?

• Published in: Expert systems with applications Vol. 188; p. 116015
• Main Authors: Ramos-Pérez, Ismael, Arnaiz-González, Álvar, Rodríguez, Juan J., García-Osorio, César
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.02.2022; Elsevier BV
• Subjects: Algorithms; Balancing; Classifiers; Feature selection; High dimensional data; Machine learning; Resampling; Unbalanced; Very low sample size; Wide data; Wide data; Feature selection; High dimensional data; Very low sample size; Unbalanced; Machine learning
• ISSN: 0957-4174; 1873-6793
• DOI: 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. •Wide datasets usually suffer from unbalanced classes distributions.•Feature selection (FS) is commonly recommended for wide datasets.•We aim to find the best combination and order to apply FS and resampling.•14 datasets, 5 classifiers, 7 FS, and 7 balancing strategies were tested.•The best configuration was SVM-RFE used before RUS for the SVM-G classifier.