In this paper, we introduce a novel data transformation framework based on
Opposition-Based Learning (OBL) to boost the performance of traditional
classification algorithms. Originally developed to accelerate convergence in
optimization tasks, OBL is leveraged here to generate synthetic opposite
samples that replace the acutely training data and improve decision boundary
formation. We explore three OBL variants; Global OBL, Class-Wise OBL, and
Localized Class-Wise OBL; and integrate them with several widely used
classifiers, including K-Nearest Neighbors (KNN), Support Vector Machines
(SVM), Logistic Regression (LR), and Decision Tree (DT). Extensive experiments
conducted on 26 heterogeneous and high-dimensional datasets demonstrate that
OBL-enhanced classifiers consistently outperform their standard counterparts in
terms of accuracy and F1-score, frequently achieving near-perfect or perfect
classification. Furthermore, OBL contributes to improved computational
efficiency, particularly in SVM and LR. These findings underscore the potential
of OBL as a lightweight yet powerful data transformation strategy for enhancing
classification performance, especially in complex or sparse learning
environments.

Este artículo explora los viajes en el tiempo y sus implicaciones.

Descargar PDF: