A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction
The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing d...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
|
| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2019/8460934 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832560558938783744 |
|---|---|
| author | Tuong Le Minh Thanh Vo Bay Vo Mi Young Lee Sung Wook Baik |
| author_facet | Tuong Le Minh Thanh Vo Bay Vo Mi Young Lee Sung Wook Baik |
| author_sort | Tuong Le |
| collection | DOAJ |
| description | The diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. Due to the class imbalance problem occurring in the bankruptcy datasets, several special techniques would be used to improve the prediction performance. Oversampling technique and cost-sensitive learning framework are two common methods for dealing with class imbalance problem. Using oversampling techniques and cost-sensitive learning framework independently also improves predictability. However, for datasets with very small balancing ratios, combining two above techniques will produce the better results. Therefore, this study develops a hybrid approach using oversampling technique and cost-sensitive learning, namely, HAOC for bankruptcy prediction on the Korean Bankruptcy dataset. The first module of HAOC is oversampling module with an optimal balancing ratio found in the first experiment that will give the best overall performance for the validation set. Then, the second module uses the cost-sensitive learning model, namely, CBoost algorithm to bankruptcy prediction. The experimental results show that HAOC will give the best performance value for bankruptcy prediction compared with the existing approaches. |
| format | Article |
| id | doaj-art-dcfee20a65764dd2a4c2f6863a06dcf1 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-dcfee20a65764dd2a4c2f6863a06dcf12025-02-03T01:27:17ZengWileyComplexity1076-27871099-05262019-01-01201910.1155/2019/84609348460934A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy PredictionTuong Le0Minh Thanh Vo1Bay Vo2Mi Young Lee3Sung Wook Baik4Digital Contents Research Institute, Sejong University, Seoul, Republic of KoreaInstitute of Research and Development, Duy Tan University, Da Nang 550000, VietnamFaculty of Information Technology, Ho Chi Minh City University of Technology (HUTECH), Ho Chi Minh, VietnamDigital Contents Research Institute, Sejong University, Seoul, Republic of KoreaDigital Contents Research Institute, Sejong University, Seoul, Republic of KoreaThe diagnosis of bankruptcy companies becomes extremely important for business owners, banks, governments, securities investors, and economic stakeholders to optimize the profitability as well as to minimize risks of investments. Many studies have been developed for bankruptcy prediction utilizing different machine learning approaches on various datasets around the world. Due to the class imbalance problem occurring in the bankruptcy datasets, several special techniques would be used to improve the prediction performance. Oversampling technique and cost-sensitive learning framework are two common methods for dealing with class imbalance problem. Using oversampling techniques and cost-sensitive learning framework independently also improves predictability. However, for datasets with very small balancing ratios, combining two above techniques will produce the better results. Therefore, this study develops a hybrid approach using oversampling technique and cost-sensitive learning, namely, HAOC for bankruptcy prediction on the Korean Bankruptcy dataset. The first module of HAOC is oversampling module with an optimal balancing ratio found in the first experiment that will give the best overall performance for the validation set. Then, the second module uses the cost-sensitive learning model, namely, CBoost algorithm to bankruptcy prediction. The experimental results show that HAOC will give the best performance value for bankruptcy prediction compared with the existing approaches.http://dx.doi.org/10.1155/2019/8460934 |
| spellingShingle | Tuong Le Minh Thanh Vo Bay Vo Mi Young Lee Sung Wook Baik A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction Complexity |
| title | A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction |
| title_full | A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction |
| title_fullStr | A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction |
| title_full_unstemmed | A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction |
| title_short | A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction |
| title_sort | hybrid approach using oversampling technique and cost sensitive learning for bankruptcy prediction |
| url | http://dx.doi.org/10.1155/2019/8460934 |
| work_keys_str_mv | AT tuongle ahybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT minhthanhvo ahybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT bayvo ahybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT miyounglee ahybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT sungwookbaik ahybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT tuongle hybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT minhthanhvo hybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT bayvo hybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT miyounglee hybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction AT sungwookbaik hybridapproachusingoversamplingtechniqueandcostsensitivelearningforbankruptcyprediction |