Dynamic Meta-Learning for Adaptive XGBoost-Neural Ensembles
Fuente:
arXiv
Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866915532502990848 |
|---|---|
| author | Sedek, Arthur |
| author_facet | Sedek, Arthur |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03301 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Meta-Learning for Adaptive XGBoost-Neural Ensembles Sedek, Arthur Machine Learning Artificial Intelligence 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. |
| title | Dynamic Meta-Learning for Adaptive XGBoost-Neural Ensembles |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2510.03301 |