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Symmetry 2018, 10(4), 79; doi:10.3390/sym10040079

Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset

Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea
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Received: 27 February 2018 / Revised: 20 March 2018 / Accepted: 21 March 2018 / Published: 22 March 2018
(This article belongs to the Special Issue Novel Machine Learning Approaches for Intelligent Big Data)
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Abstract

In recent years, weakened by the fall of economic growth, many enterprises fell into the crisis caused by financial difficulties. Bankruptcy prediction, a machine learning model, is a great utility for financial institutions, fund managers, lenders, governments, and economic stakeholders. Due to the number of bankrupt companies compared to that of non-bankrupt companies, bankruptcy prediction faces the problem of imbalanced data. This study first presents the bankruptcy prediction framework. Then, five oversampling techniques are used to deal with imbalance problems on the experimental dataset which were collected from Korean companies in two years from 2016 to 2017. Experimental results show that using oversampling techniques to balance the dataset in the training stage can enhance the performance of the bankruptcy prediction. The best overall Area Under the Curve (AUC) of this framework can reach 84.2%. Next, the study extracts more features by combining the financial dataset with transaction dataset to increase the performance for bankruptcy prediction and achieves 84.4% AUC. View Full-Text
Keywords: bankruptcy prediction; imbalanced data; machine learning; oversampling techniques; transaction dataset bankruptcy prediction; imbalanced data; machine learning; oversampling techniques; transaction dataset
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Le, T.; Lee, M.Y.; Park, J.R.; Baik, S.W. Oversampling Techniques for Bankruptcy Prediction: Novel Features from a Transaction Dataset. Symmetry 2018, 10, 79.

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