Abstract
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.
Highlights
Financial risk prediction has been a hot topic for years due to its great importance [1]–[4]
2) SMOTEBoost-C4.5, Synthetic Minority Oversampling Technique (SMOTE)- C4.5, and SMOTE- multilayer perceptron (MLP) are ranked as the top three classifiers based on their performances on the six criteria
Default and bankruptcy are rare events compared to normal accounts and companies functioning well, which indicate that financial risk data are imbalanced by nature
Summary
Financial risk prediction has been a hot topic for years due to its great importance [1]–[4]. Many methods have been developed to learn from imbalanced data sets over the decades. They can be categorized into three major groups: resampling, cost-sensitive learning, and ensemble techniques. Ensemble learning techniques, which have demonstrated notable improvement over a single classification algorithm, have been applied to financial risk classification. Sun and Li [20] investigated weighted majority voting combination of multiple diversified classifiers and obtained higher average accuracy than any base classifier. Bagging and Boosting-based ensemble methods have been received increasing attention [24]–[27]. Sun et al [27] established AdaBoost ensemble respectively with single attribute test (SAT) and DT and found that AdaBoost-SAT outperformed AdaBoost-DT
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
