A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction
المؤلفون المشاركون
Le, Tuong
Baik, Sung Wook
Lee, Mi Young
Vo, Minh Thanh
Vo, Bay
المصدر
العدد
المجلد 2019، العدد 2019 (31 ديسمبر/كانون الأول 2019)، ص ص. 1-12، 12ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2019-08-05
دولة النشر
مصر
عدد الصفحات
12
التخصصات الرئيسية
الملخص EN
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.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Le, Tuong& Vo, Minh Thanh& Vo, Bay& Lee, Mi Young& Baik, Sung Wook. 2019. A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction. Complexity،Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132954
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Le, Tuong…[et al.]. A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction. Complexity No. 2019 (2019), pp.1-12.
https://search.emarefa.net/detail/BIM-1132954
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Le, Tuong& Vo, Minh Thanh& Vo, Bay& Lee, Mi Young& Baik, Sung Wook. A Hybrid Approach Using Oversampling Technique and Cost-Sensitive Learning for Bankruptcy Prediction. Complexity. 2019. Vol. 2019, no. 2019, pp.1-12.
https://search.emarefa.net/detail/BIM-1132954
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1132954
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر