| Resumen |
This paper presents the development of the N-Spherical Minimalist Machine Learning (MML) classifier, an innovative model within the Minimalist Machine Learning paradigm. Using N-spherical coordinates and concepts from metaheuristics and associative models, this classifier effectively addresses challenges such as data dimensionality and class imbalance in complex datasets. Performance evaluations using the F1 measure and balanced accuracy demonstrate its superior efficiency and robustness compared to state-of-the-art classifiers. Statistical validation is conducted using the Friedman and Holm tests. Although currently limited to binary classification, this work highlights the potential of minimalist approaches in machine learning for classification of highly dimensional and imbalanced data. Future extensions aim to include multi-class problems and mechanisms for handling categorical data. Copyright © 2025 Jimenez-Cruz, Yáñez-Márquez, Gonzalez-Mendoza, Villuendas-Rey and Monroy. |
