Title | ||
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A MCDM-Based Evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction. |
Abstract | ||
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Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision making (MCDM)-based approach to evaluate imbalanced classifiers in credit and bankruptcy risk prediction by considering multiple performance metrics simultaneously. An experimental study is designed to provide a comprehensive evaluation of imbalanced classifiers using the proposed evaluation approach over seven financial imbalanced data sets from the UCI Machine Learning Repository. The TOPSIS, a well-known MCDM method, was applied to rank three categories of imbalanced classifiers using six popular evaluation criteria. The rankings results indicate that: 1) the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion; and 2) Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of imbalanced learning approaches. Specifically, SMOTEBoost-C4.5, SMOTE-C4.5, and SMOTE-MLP were ranked as the top three classifiers based on their performances on the six criteria. |
Year | DOI | Venue |
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2019 | 10.1109/ACCESS.2019.2924923 | IEEE ACCESS |
Keywords | Field | DocType |
Financial risk prediction,imbalanced classification,multiple criteria decision making (MCDM),algorithm evaluation | Financial risk,Multiple-criteria decision analysis,Computer science,Artificial intelligence,Machine learning,Distributed computing | Journal |
Volume | ISSN | Citations |
7 | 2169-3536 | 0 |
PageRank | References | Authors |
0.34 | 0 | 2 |
Name | Order | Citations | PageRank |
---|---|---|---|
Yongming Song | 1 | 5 | 1.05 |
Yi Peng | 2 | 1303 | 78.20 |