An Ensemble Resampling Based Transfer AdaBoost Algorithm for Small Sample Credit Classification with Class Imbalance

Xiaoming Zhang (), Lean Yu () and Hang Yin ()
Additional contact information
Xiaoming Zhang: Jiangxi University of Finance and Economics
Lean Yu: Sichuan University
Hang Yin: Harbin Engineering University

Computational Economics, 2025, vol. 65, issue 6, No 24, 3779-3806

Abstract: Abstract It is prone to overfitting and poor generalization ability for imbalanced small sample datasets in modeling. Auxiliary data is an effective solution. However, there may be data distribution differences between auxiliary data and small sample data, and the presence of noise samples affects the prediction performance. To address this issue, we propose an ensemble resampling based transfer AdaBoost (TrAdaBoost) algorithm for imbalanced small sample credit classification. The proposed algorithm framework has two stages: ensemble resampling dataset generation and weight adaptive transfer AdaBoost (WATrA) model prediction. In the first stage, neighborhood-based resampling technique is proposed to filter source data and reduce noise samples, followed by bagging resampling to balance the filtered source data. In the second stage, a weight adaptive TrAdaBoost model is utilized to address small sample with class imbalance issues and improve the effectiveness of the proposed method. We validate the proposed algorithm on two small sample credit datasets with class imbalance, and observe significant improvements in performance compared to traditional supervised machine learning methods and resampling methods based on the main evaluation criteria.

Keywords: Small sample; Class imbalance; Ensemble resampling; Weight adaptive TrAdaBoost; Credit classification (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s10614-024-10690-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:kap:compec:v:65:y:2025:i:6:d:10.1007_s10614-024-10690-6

Ordering information: This journal article can be ordered from
http://www.springer. ... ry/journal/10614/PS2

DOI: 10.1007/s10614-024-10690-6

Access Statistics for this article

Computational Economics is currently edited by Hans Amman

More articles in Computational Economics from Springer, Society for Computational Economics Contact information at EDIRC.
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().