Work

Neural-NGBoost: Natural gradient boosting with neural network base learners

Year: 2025

Type: article

Abstract: NGBoost has shown promising results in probabilistic and point estimation tasks. However, it is vague still whether this method can be scalable to neural architecture system since its base learner is based on decision trees. To resolve this, we design a Neural-NGBoost framework by replacing the base learner with lightweight neural networks and introducing joint gradient estimation for boosting procedure. Based on natural gradient boosting, we iteratively update the neural based learner by inferring natural gradient and update the parameter score with its probabilistic distribution. Experimental results show Neural-NGBoost achieves superior performance across various datasets compared to other boosting methods.

Source: ICT Express

DOI: 10.1016/j.icte.2025.08.003

Authors: Jamshidjon Ganiev, Deok-Woong Kim, Seung-Hwan Bae

Institution: Inha University

Language: English

Topic: Machine Learning and Data Classification

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,500