Abstract
Many practical applications suffer from imbalanced data classification, in which case the minority class has degraded recognition rate. The primary causes are the sample scarcity of the minority class and the intrinsic complex distribution characteristics of imbalanced datasets. The imbalanced classification problem is more serious on small sample datasets. To solve the problems of small sample and class imbalance, a hybrid resampling method is proposed. The proposed method combines an oversampling approach (synthetic minority oversampling technique, SMOTE) and a novel data cleaning approach (weighted edited nearest neighbor rule, WENN). First, SMOTE generates synthetic minority class examples using linear interpolation. Then, WENN detects and deletes unsafe majority and minority class examples using weighted distance function and k-nearest neighbor (kNN) rule. The weighted distance function scales up a commonly used distance by considering local imbalance and spacial sparsity. Extensive experiments over synthetic and real datasets validate the superiority of the proposed SMOTE-WENN compared with three state-of-the-art resampling methods.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China (Nos: 61872203 and 61802212), the Shandong Provincial Natural Science Foundation (No: ZR2019BF017), Major Scientific and Technological Innovation Projects of Shandong Province (Nos: 2019JZZY010127, 2019JZZY010132 and 2019JZZY010201), Jinan City “20 universities” Funding Projects Introducing Innovation Team Program (No: 2019GXRC031), and the Project of Shandong Province Higher Educational Science and Technology Program (No: J18KA331).
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Guan, H., Zhang, Y., Xian, M. et al. SMOTE-WENN: Solving class imbalance and small sample problems by oversampling and distance scaling. Appl Intell 51, 1394–1409 (2021). https://doi.org/10.1007/s10489-020-01852-8
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DOI: https://doi.org/10.1007/s10489-020-01852-8