2026-06-28T01:13:30Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/112822026-01-27T01:05:38Zcom_10259_4219com_10259_5086com_10259_2604com_10259_5377col_10259_4220col_10259_5378

Ramos Pérez, Ismael Barbero Aparicio, José Antonio Canepa Oneto, Antonio Jesús Arnaiz González, Álvar Maudes Raedo, Jesús M. 2026-01-26T08:14:11Z 2026-01-26T08:14:11Z 2024-04 2078-2489 https://hdl.handle.net/10259/11282 10.3390/info15040223 2078-2489 The most common preprocessing techniques used to deal with datasets having high dimensionality and a low number of instances—or wide data—are feature reduction (FR), feature selection (FS), and resampling. This study explores the use of FR and resampling techniques, expanding the limited comparisons between FR and filter FS methods in the existing literature, especially in the context of wide data. We compare the optimal outcomes from a previous comprehensive study of FS against new experiments conducted using FR methods. Two specific challenges associated with the use of FR are outlined in detail: finding FR methods that are compatible with wide data and the need for a reduction estimator of nonlinear approaches to process out-of-sample data. The experimental study compares 17 techniques, including supervised, unsupervised, linear, and nonlinear approaches, using 7 resampling strategies and 5 classifiers. The results demonstrate which configurations are optimal, according to their performance and computation time. Moreover, the best configuration—namely, k Nearest Neighbor (KNN) + the Maximal Margin Criterion (MMC) feature reducer with no resampling—is shown to outperform state-of-the-art algorithms. This work was supported by the Junta de Castilla y León under project BU055P20 (JCyL/FEDER, UE) and by the Ministry of Science and Innovation under project PID2020-119894GB-I00, co-financed through European Union FEDER funds. Ismael Ramos-Pérez is funded through a pre-doctoral grant by the Universidad de Burgos. application/pdf eng MDPI Information. 2024, V. 15, n. 4, 223 https://doi.org/10.3390/info15040223 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess Feature selection Feature reduction Wide data High dimensional data Imbalanced data Machine learning Informática Inteligencia artificial Computer science Artificial intelligence An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Information 15 4 223