RT info:eu-repo/semantics/article
T1 An Extensive Performance Comparison between Feature Reduction and Feature Selection Preprocessing Algorithms on Imbalanced Wide Data
A1 Ramos Pérez, Ismael
A1 Barbero Aparicio, José Antonio
A1 Canepa Oneto, Antonio Jesús
A1 Arnaiz González, Álvar
A1 Maudes Raedo, Jesús M.
K1 Feature selection
K1 Feature reduction
K1 Wide data
K1 High dimensional data
K1 Imbalanced data
K1 Machine learning
K1 Informática
K1 Computer science
K1 Inteligencia artificial
K1 Artificial intelligence
AB 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.
PB MDPI
SN 2078-2489
YR 2024
FD 2024-04
LK https://hdl.handle.net/10259/11282
UL https://hdl.handle.net/10259/11282
LA eng
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 22-abr-2026