A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method

Xiaoxia Wu; Dongqi Yang; Wenyu Zhang; Shuai Zhang

doi:doi:10.11648/j.ijics.20190403.12

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A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method

Xiaoxia Wu, Dongqi Yang, Wenyu Zhang, Shuai Zhang

Published in International Journal of Information and Communication Sciences (Volume 4, Issue 3)

Received: 5 August 2019 Published: 27 September 2019

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Abstract

The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).

Published in	International Journal of Information and Communication Sciences (Volume 4, Issue 3)
DOI	10.11648/j.ijics.20190403.12
Page(s)	52-58
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2019. Published by Science Publishing Group

Keywords

Data Mining, Ensemble Learning, Corporate Bankruptcy, Prediction

References

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APA Style

Xiaoxia Wu, Dongqi Yang, Wenyu Zhang, Shuai Zhang. (2019). A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method. International Journal of Information and Communication Sciences, 4(3), 52-58. https://doi.org/10.11648/j.ijics.20190403.12

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ACS Style

Xiaoxia Wu; Dongqi Yang; Wenyu Zhang; Shuai Zhang. A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method. Int. J. Inf. Commun. Sci. 2019, 4(3), 52-58. doi: 10.11648/j.ijics.20190403.12

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AMA Style

Xiaoxia Wu, Dongqi Yang, Wenyu Zhang, Shuai Zhang. A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method. Int J Inf Commun Sci. 2019;4(3):52-58. doi: 10.11648/j.ijics.20190403.12

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@article{10.11648/j.ijics.20190403.12,
  author = {Xiaoxia Wu and Dongqi Yang and Wenyu Zhang and Shuai Zhang},
  title = {A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method},
  journal = {International Journal of Information and Communication Sciences},
  volume = {4},
  number = {3},
  pages = {52-58},
  doi = {10.11648/j.ijics.20190403.12},
  url = {https://doi.org/10.11648/j.ijics.20190403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20190403.12},
  abstract = {The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).},
 year = {2019}
}

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TY  - JOUR
T1  - A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method
AU  - Xiaoxia Wu
AU  - Dongqi Yang
AU  - Wenyu Zhang
AU  - Shuai Zhang
Y1  - 2019/09/27
PY  - 2019
N1  - https://doi.org/10.11648/j.ijics.20190403.12
DO  - 10.11648/j.ijics.20190403.12
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 52
EP  - 58
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20190403.12
AB  - The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).
VL  - 4
IS  - 3
ER  -

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Author Information

Xiaoxia Wu

Department of Financial Accounting, Zhejiang Institute of Economics and Trade, Hangzhou, China
Dongqi Yang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Wenyu Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China
Shuai Zhang

School of Information, Zhejiang University of Finance and Economics, Hangzhou, China

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Plain Text BibTeX RIS

APA Style

Xiaoxia Wu, Dongqi Yang, Wenyu Zhang, Shuai Zhang. (2019). A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method. International Journal of Information and Communication Sciences, 4(3), 52-58. https://doi.org/10.11648/j.ijics.20190403.12

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ACS Style

Xiaoxia Wu; Dongqi Yang; Wenyu Zhang; Shuai Zhang. A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method. Int. J. Inf. Commun. Sci. 2019, 4(3), 52-58. doi: 10.11648/j.ijics.20190403.12

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AMA Style

Xiaoxia Wu, Dongqi Yang, Wenyu Zhang, Shuai Zhang. A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method. Int J Inf Commun Sci. 2019;4(3):52-58. doi: 10.11648/j.ijics.20190403.12

Copy | Download

@article{10.11648/j.ijics.20190403.12,
  author = {Xiaoxia Wu and Dongqi Yang and Wenyu Zhang and Shuai Zhang},
  title = {A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method},
  journal = {International Journal of Information and Communication Sciences},
  volume = {4},
  number = {3},
  pages = {52-58},
  doi = {10.11648/j.ijics.20190403.12},
  url = {https://doi.org/10.11648/j.ijics.20190403.12},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijics.20190403.12},
  abstract = {The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).},
 year = {2019}
}

Copy | Download

TY  - JOUR
T1  - A Hybrid Ensemble Model for Corporate Bankruptcy Prediction Based on Feature Engineering Method
AU  - Xiaoxia Wu
AU  - Dongqi Yang
AU  - Wenyu Zhang
AU  - Shuai Zhang
Y1  - 2019/09/27
PY  - 2019
N1  - https://doi.org/10.11648/j.ijics.20190403.12
DO  - 10.11648/j.ijics.20190403.12
T2  - International Journal of Information and Communication Sciences
JF  - International Journal of Information and Communication Sciences
JO  - International Journal of Information and Communication Sciences
SP  - 52
EP  - 58
PB  - Science Publishing Group
SN  - 2575-1719
UR  - https://doi.org/10.11648/j.ijics.20190403.12
AB  - The bankruptcy of manufacturing corporates is an important factor affecting economic stability. Corporate bankruptcy has become a hot research topic mainly through financial data analysis and prediction. With the development of data science and artificial intelligence, machine learning technology helps researchers improve the accuracy and robustness of classification models. Ensemble learning, with its strong predictive power and robustness, plays an important role in machine learning and binary classification prediction. In this study, we proposed a bankruptcy classification model combining feature engineering method and ensemble learning method, Synthetic Minority Oversampling Technique (SMOTE) imbalanced data learning algorithm is applied to generate balanced dataset, multi-interval discretization filter is applied to enhance the interpretability of the features and ensemble learning method is applied to get an accurate and objective prediction. To demonstrate the validity and performance of the proposed model, we conducted comparative experiments with ten other baseline classifiers, proving that SMOTE imbalanced learning algorithm and feature engineering method with multi-interval discretization was effective. The comparative experiment results show that the ensemble learning method has a good effect on improving the performance of the proposed model. The final results show that the proposed model has achieved better performance and robustness than other baseline classifiers in terms of classification accuracy, F-measure and Area under Curve (AUC).
VL  - 4
IS  - 3
ER  -

Copy | Download