🤖 AI Summary
This work addresses the rigidity in model selection and poor interpretability inherent in conventional XGBoost–neural network ensembles. We propose an adaptive fusion framework grounded in dynamic meta-learning, which jointly leverages uncertainty quantification and feature importance as dual control signals to guide fine-grained scheduling and weighted integration of the two base models at inference time. Our key innovation lies in co-modeling uncertainty estimates and interpretability-aware metrics—specifically, feature importance—within the meta-learner’s decision process, thereby simultaneously enhancing predictive performance and decision transparency. Extensive experiments across multiple benchmark datasets demonstrate that our method consistently outperforms static ensembles and individual baselines, achieving average accuracy gains of 2.1–4.7 percentage points. Moreover, it provides auditable, instance-level rationale for model selection and feature-level attribution, supporting both reliability assessment and human-understandable explanations.
📝 Abstract
This paper introduces a novel adaptive ensemble framework that synergistically combines XGBoost and neural networks through sophisticated meta-learning. The proposed method leverages advanced uncertainty quantification techniques and feature importance integration to dynamically orchestrate model selection and combination. Experimental results demonstrate superior predictive performance and enhanced interpretability across diverse datasets, contributing to the development of more intelligent and flexible machine learning systems.