Combining Multi-ratio Undersampling and Metric Learning for Imbalanced Classification (pp462-475)
         Takahiro Komamizu
         doi: https://doi.org/10.26421/JDI2.4-5
Abstracts: In classification, class imbalance is a factor that degrades the classification performance of many classification methods. Resampling is one widely accepted approach to the class imbalance; however, it still suffers from an insufficient data space, which also degrades performance. To overcome this, in this paper, an undersampling-based imbalanced classification framework, MMEnsemble, is proposed that incorporates metric learning into a multi-ratio undersampling-based ensemble. This framework also overcomes a problem with determining the appropriate sampling ratio in the multi-ratio ensemble method. It was evaluated by using 12 real-world datasets. It outperformed the state-of-the-art approaches of metric learning, undersampling, and oversampling in recall and ROC-AUC, and it performed comparably with them in terms of  Gmean and F-measure metrics.
Key words: imbalanced classification, undersampling, ensemble, metric learning