RT Journal Article
T1 Pattern recognition frequency-based feature selection with multi-objective discrete evolution strategy for high-dimensional medical datasets
A1 Nematzadeh, Hossein
A1 García-Nieto, José Manuel
A1 Aldana-Montes, José Francisco
A1 Navas-Delgado, Ismael
K1 Reconocimiento de formas (Informática)
K1 Sistemas informáticos
AB Feature selection has a prominent role in high-dimensional datasets to increase classification accuracy, decrease the learning algorithm computational time, and present the most informative features to decision-makers. This paper proposes a two-stage hybrid feature selection for high-dimensional medical datasets: Maximum Pattern Recognition - Multi-objective Discrete Evolution Strategy (MPR-MDES). MPR is a rapid filter ranker that significantly outperforms existing frequency-based rankers in recognizing non-linear patterns, effectively eliminating a majority of non-informative features. Then, the wrapper Multi-objective Discrete Evolution Strategy (MDES) uses the remaining features and obtains sets of solutions which are automatically presented to decision-makers. The experiments conducted on large medical datasets demonstrate that MPR-MDES achieves considerable improvements compared to state-of-the-art methods, in terms of both classification accuracy and dimensionality reduction. In this sense, the proposal successfully performs when presenting informative feature sets to decision-makers. The implementation is available on https://github.com/KhaosResearch/MPR-MDES.
PB Elsevier
YR 2024
FD 2024-02-17
LK https://hdl.handle.net/10630/30824
UL https://hdl.handle.net/10630/30824
LA eng
NO Hossein Nematzadeh, José García-Nieto, José F. Aldana-Montes, Ismael Navas-Delgado, Pattern recognition frequency-based feature selection with multi-objective discrete evolution strategy for high-dimensional medical datasets, Expert Systems with Applications, Volume 249, Part A, 2024, 123521, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2024.123521
NO Funding for open access charge: Universidad de Málaga/CBUA .This work has been partially funded by grants (funded by MCIN/AEI/10.13039/501100011033/) PID2020-112540RB-C41, AETHER-UMA (A smart data holistic approach for context-aware data analytics: semantics and context exploitation), and QUAL21 010UMA (Junta de Andalucía).
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026