2026-05-07T08:14:25Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/112822026-01-27T01:05:38Zcom_10259_4219com_10259_5086com_10259_2604com_10259_5377col_10259_4220col_10259_5378
Repositorio Institucional de la Universidad de Burgos
author
Ramos Pérez, Ismael
author
Barbero Aparicio, José Antonio
author
Canepa Oneto, Antonio Jesús
author
Arnaiz González, Álvar
author
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.
eng
Atribución 4.0 Internacional
Feature selection
Feature reduction
Wide data
High dimensional data
Imbalanced data
Machine learning
An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data
info:eu-repo/semantics/article
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URL
https://riubu.ubu.es/bitstream/10259/11282/1/Ramos-information_2024.pdf
File
MD5
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553851
application/pdf
Ramos-information_2024.pdf