BPE: Behavioral Profiling Ensemble

Ensemble learning is widely recognized as a pivotal strategy for pushing the boundaries of predictive performance. Traditional static ensemble methods, such as Stacking, typically assign weights by treating each base learner as a holistic entity, thereby overlooking the fact that individual models exhibit varying degrees of competence across different regions of the instance space. To address this limitation, Dynamic Ensemble Selection (DES) was introduced. However, both static and dynamic approaches predominantly rely on the divergence among different models as the basis for integration. This inter-model perspective neglects the intrinsic characteristics of the models themselves and necessitates a heavy reliance on validation sets for competence estimation. In this paper, we propose the Behavioral Profiling Ensemble (BPE) framework, which introduces a novel paradigm shift. Unlike traditional methods, BPE constructs a ``behavioral profile'' intrinsic to each model and derives integration weights based on the deviation between the model's response to a specific test instance and its established behavioral profile. Extensive experiments on both synthetic and real-world datasets demonstrate that the algorithm derived from the BPE framework achieves significant improvements over state-of-the-art ensemble baselines. These gains are evident not only in predictive accuracy but also in computational efficiency and storage resource utilization across various scenarios.