OCEG: an Interactive Visualization System for Classifier Ensemble Generation - Diaz, Mauro; Camara-Chavez, Guillermo; Gomez-Nieto, Erick (Anais da Conference on Graphics, Patterns and Images (SIBGRAPI), 2025)
Classification is a fundamental task in machine learning. However, individual models can struggle to deliver accurate results in various scenarios. Ensemble methods, which combine multiple classifiers, can enhance performance, but selecting and fine-tuning these models can be time-consuming. We introduce OCEG, a visual system designed to support the interactive exploration and composition of classifier ensembles. OCEG features a web-based interface that utilizes visual cues and pruning algorithms to highlight differences between models, automatically optimize ensemble parameters, and compare alternatives from a comprehensive perspective. We validate our system through quantitative experiments, a real-world case study, and user evaluations.