Dynamic Meta-Learning for Adaptive XGBoost-Neural Ensembles

• URL: http://arxiv.org/abs/2510.03301v1
• Date: Tue, 30 Sep 2025 07:45:49 GMT
• Title: Dynamic Meta-Learning for Adaptive XGBoost-Neural Ensembles
• Authors: Arthur Sedek,
• Abstract summary: This paper introduces a novel adaptive ensemble framework that synergistically combines XGBoost and neural networks through sophisticated meta-learning.<n> Experimental results demonstrate superior predictive performance and enhanced interpretability across diverse datasets.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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