Work

Hybrid ensemble machine learning models

Year: 2025

Type: article

Abstract: Machine learning models are usually assessed and compared in terms of predictive performance. Ensemble models, which average the predictions obtained from different models, often improve such performance. In this paper we show how to further improve the predictive accuracy of ensemble models, and allow them to achieve strong performance without retraining. To this aim we leverage the diversity among individual models, expressed by their covariance, computed on a subsample of the data ordered by the best model. We illustrate our proposal with applications to real data.

Source: Physica A Statistical Mechanics and its Applications

DOI: 10.1016/j.physa.2025.131083

Authors: Paolo Giudici, Francesca Mariani, Gloria Polinesi

Institutions: University of Pavia, Marche Polytechnic University

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,550

Funder: European Commission