Abstract: The high dimensionality of modern datasets presents significant challenges for machine learning, including increased computational cost, model complexity, and risk of overfitting. This study introduces a metaheuristic framework for optimized dimensionality reduction to identify the highly discriminative feature subsets. The proposed method (KDR-PSO) combines a Particle Swarm Optimization (PSO) algorithm with the K-Nearest Neighbors Distance Ratio (KDR) as a filter-based objective function. This metric quantitatively assesses class separability within a feature subspace by computing the ratio of the average distance from a sample to neighbors in other classes versus those in its own class. Maximizing this ratio with a penalty for model size, KDR-PSO automates the discovery of parsimonious feature sets that maximize inter-class discrimination. The method is computationally efficient, naturally lending itself to multi-class classification and avoiding the prohibitive cost associated with classifier-in-the-loop wrappers. Experimental results on benchmark gene expression and image datasets show that KDR-PSO can achieve better dimensionality reduction compared to baselines and other algorithms, such as winning a better or at least similar performing models with decreased features. This approach is a strong and pragmatic technique to improve the model interpretability and generalizability for high-dimensional regions.

Eman Abdulazeem Ahmed, Malek Alzaqebah and Sana Jawarneh. “Optimized Dimensionality Reduction Using Metaheuristic and Class Separability”. International Journal of Advanced Computer Science and Applications (IJACSA) 16.12 (2025). http://dx.doi.org/10.14569/IJACSA.2025.0161219

@article{Ahmed2025,
title = {Optimized Dimensionality Reduction Using Metaheuristic and Class Separability},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0161219},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0161219},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {12},
author = {Eman Abdulazeem Ahmed and Malek Alzaqebah and Sana Jawarneh}
}