Advancing Bankruptcy Forecasting With Hybrid Machine Learning Techniques: Insights From an Unbalanced Polish Dataset
The challenge of bankruptcy prediction, critical for averting financial sector losses, is amplified by the prevalence of imbalanced datasets, which often skew prediction models. Addressing this, our study introduces the innovative hybrid model XGBoost+ANN, designed to leverage the strengt...
Main Authors: | Ummey Hany Ainan, Lip Yee Por, Yen-Lin Chen, Jing Yang, Chin Soon Ku |
---|---|
Format: | Article |
Language: | English |
Published: |
IEEE
2024-01-01
|
Series: | IEEE Access |
Subjects: | |
Online Access: | https://ieeexplore.ieee.org/document/10399785/ |
Similar Items
-
Application of models for forecasting of enterprise bankruptcy
by: Kristina Garškaitė
Published: (2008-12-01) -
Applying, updating and comparing bankruptcy forecasting models. The case of Greece
by: Nikolaos Daskalakis, et al.
Published: (2022-09-01) -
A novel hybrid ensemble convolutional neural network for face recognition by optimizing hyperparameters
by: Anwarul Shahina, et al.
Published: (2023-06-01) -
Comparing the Performance of Machine Learning and Deep Learning Algorithms in Wastewater Treatment Process
by: Jaeil Kim, et al.
Published: (2023-12-01) -
A Clustering Based Classifier Ensemble Approach to Corporate Bankruptcy Prediction
by: Aytuğ Onan
Published: (2018-12-01)