A comprehensive ensemble pruning framework based on dual-objective maximization trade-off

Ensemble learning has gotten a lot of interest because of its capacity to increase predictive accuracy by merging numerous models. However, redundant data and a high level of computing complexity frequently plague ensembles. To choose a subset of models while maintaining the accuracy and diversity of the ensemble, ensemble pruning techniques are used to address these problems. Accuracy and diversity must coexist, even though their goals are conflicting. This is why we formulate the issue of ensemble pruning as a dual-objective maximization problem using the idea from information theory. Then, we propose a Comprehensive Ensemble Pruning Framework (CEPF) based on the dual-objective maximization (DOM) trade-off metric. Extensive evaluation of our framework on the exclusively collected PhysioSense dataset demonstrates the superiority of our method compared to existing pruning techniques. PhysioSense dataset was collected after getting approval from the Institutional Human Ethics Committee (IHEC) of Panimalar Medical College Hospital and Research Institute, Chennai, Tamil Nadu (Protocol No: PMCHRI-IHEC-059). The proposed framework not only preserves or improves ensemble accuracy and diversity but also achieves a significant reduction in actual ensemble size. Furthermore, the proposed method provides valuable insights into the dual-objective trade-off between accuracy and diversity paving the way for further research and advancements in ensemble pruning techniques.