# Dynamic Ensemble Learning for Credit Scoring: A Comparative Study

arXiv: https://arxiv.org/abs/2010.08930 · HTML: https://mlsanity.com/paper/2010.08930/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2020.

## Built on by (most cited first, 1 of 1)

- [Verification of Adaptive Agentic Controllers through Finite Rule Revision](https://mlsanity.com/paper/2607.09770/) (2026)

## Abstract (arXiv)

Automatic credit scoring, which assesses the probability of default by loan applicants, plays a vital role in peer-to-peer lending platforms to reduce the risk of lenders. Although it has been demonstrated that dynamic selection techniques are effective for classification tasks, the performance of these techniques for credit scoring has not yet been determined. This study attempts to benchmark different dynamic selection approaches systematically for ensemble learning models to accurately estimate the credit scoring task on a large and high-dimensional real-life credit scoring data set. The results of this study indicate that dynamic selection techniques are able to boost the performance of ensemble models, especially in imbalanced training environments.