RT Journal Article
T1 Feature selection using a classification error impurity algorithm and an adaptive genetic algorithm improved with an external repository
A1 Nematzadeh, Hossein
A1 García-Nieto, José Manuel
A1 Navas-Delgado, Ismael
A1 Aldana-Montes, José Francisco
K1 Algoritmos genéticos
K1 Programación genética (Informática)
K1 Sistemas expertos
K1 Ingeniería del software
AB Feature selection in small-sample high-dimensional datasets enhances classification accuracy and reduces computational time for model training. This paper introduces the filter Classification Error Impurity (CEI) as a frequency-based ranker that improves upon existing methods by better identifying non-linear patterns. According to this, the top features identified by the ensemble of CEI, along with Mutual Information (MI) and Fisher Ratio (FR), form the feature space utilized by the Adaptive Genetic Algorithm with External Repository (AGAwER) to identify the optimal feature combination in a wrapper approach. AGAwER leverages an external repository, incorporating the best solutions to enrich the Genetic Algorithm’s (GA) population, thus promoting diversity and enhancing exploration. As a result, a hybrid method called CMF-AGAwER is proposed, which surpasses existing modern feature selection methods. The implementation and data are accessible on GitHub at https://github.com/KhaosResearch/CMF-AGAwER.
PB Elsevier
YR 2024
FD 2024
LK https://hdl.handle.net/10630/32475
UL https://hdl.handle.net/10630/32475
LA eng
NO Hossein Nematzadeh, José García-Nieto, Ismael Navas-Delgado, José F. Aldana-Montes, Feature selection using a classification error impurity algorithm and an adaptive genetic algorithm improved with an external repository, Knowledge-Based Systems, Volume 301, 2024, 112345, ISSN 0950-7051, https://doi.org/10.1016/j.knosys.2024.112345
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 19 ene 2026