2026-05-11T15:50:33Zhttps://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 751 600 0000-0002-0330-1605 Barbero Aparicio, José Antonio 819 600 0000-0002-3269-0806 Canepa Oneto, Antonio Jesús 818 600 0000-0002-0608-2743 Arnaiz González, Álvar 39 600 0000-0001-6965-0237 Maudes Raedo, Jesús M. 352 600 0000-0001-8808-412X 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 THUMBNAIL Ramos-information_2024.pdf.jpg Ramos-information_2024.pdf.jpg IM Thumbnail image/jpeg 4642 https://riubu.ubu.es/bitstream/10259/11282/4/Ramos-information_2024.pdf.jpg 91349c3665e34e4834dda6d2472fd82d MD5 4 LICENSE license.txt license.txt text/plain; charset=utf-8 999 https://riubu.ubu.es/bitstream/10259/11282/3/license.txt b295bcbce42e2caabeb0c623d3860c06 MD5 3 CC-LICENSE license_rdf license_rdf application/rdf+xml; charset=utf-8 908 https://riubu.ubu.es/bitstream/10259/11282/2/license_rdf 0175ea4a2d4caec4bbcc37e300941108 MD5 2 ORIGINAL Ramos-information_2024.pdf Ramos-information_2024.pdf application/pdf 553851 https://riubu.ubu.es/bitstream/10259/11282/1/Ramos-information_2024.pdf b973d3a8dbd648ff80ae14896e5597b9 MD5 1 10259/11282 oai:riubu.ubu.es:10259/11282 2026-01-27 02:05:38.24 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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