Combining weighted SMOTE with ensemble learning for the class-imbalanced prediction of small business credit risk

Mohammad Zoynul Abedin,Chi Guotai,Petr Hajek,Tong Zhang

doi:10.1007/s40747-021-00614-4

Abstract

In small business credit risk assessment, the default and nondefault classes are highly imbalanced. To overcome this problem, this study proposes an extended ensemble approach rooted in the weighted synthetic minority oversampling technique (WSMOTE), which is called WSMOTE-ensemble. The proposed ensemble classifier hybridizes WSMOTE and Bagging with sampling composite mixtures to guarantee the robustness and variability of the generated synthetic instances and, thus, minimize the small business class-skewed constraints linked to default and nondefault instances. The original small business dataset used in this study was taken from 3111 records from a Chinese commercial bank. By implementing a thorough experimental study of extensively skewed data-modeling scenarios, a multilevel experimental setting was established for a rare event domain. Based on the proper evaluation measures, this study proposes that the random forest classifier used in the WSMOTE-ensemble model provides a good trade-off between the performance on default class and that of nondefault class. The ensemble solution improved the accuracy of the minority class by 15.16% in comparison with its competitors. This study also shows that sampling methods outperform nonsampling algorithms. With these contributions, this study fills a noteworthy knowledge gap and adds several unique insights regarding the prediction of small business credit risk.

Highlights

Many researchers to date have aspired to elaborate classifiers for large corporate firms or firms that were already listed
This study focuses on the credit risk assessment of small business loans in this environment, in order to advance the performance of an assessment model and improve its classification accuracy
Small enterprises are subject to minimal legal requirements for data disclosure, and it is difficult for commercial banks to get detailed information about them

Summary

Introduction

Many researchers to date have aspired to elaborate classifiers for large corporate firms or firms that were already listed. The proposed ensemble classifier hybridizes WSMOTE and Bagging with sampling composite mixtures (SCMs) to minimize the class-skewed constraints linked to positive and negative small business instances It increases the multiplicity of executed algorithms as different SCMs are applied to form diverse training sets [1]. The present study picked up the seven best WSMOTE-ensemble fusionsampling instances (IC9 to IC15) based on the highest accuracies from the C4.5 classifier It includes the number of selected Bags (B#37, B#29,...,B#35) and their respective samples. The appropriate data level was used, and the hybrid sampling strategies were SMOTE, WSMOTE, WSMOTE-ensemble, RUS, MChanUS, USOS, and RUSSMOTE AUC 0.8346(6) 0.9846(1) 0.9594(3) 0.9598(2) 0.7847(8) 0.9430(4) 0.9062(5) 0.8065(7)

TPrate

Findings

Conclusion