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.
- Takahiro Komamizu , Yasuhiro Ogawa, Katsuhiko Toyama, “An Ensemble Framework of Multi-ratio Undersampling-based Imbalanced Classification”, JDI, Vol.2, No.1, pp.30-46, 2021 (DOI )
Japanese Domestic Journals
- Takahiro Yamakoshi, Takahiro Komamizu , Yasuhiro Ogawa, Katsuhiko Toyama, “Japanese Mistakable Legal Term Correction using Infrequency-aware BERT Classifier”, Transactions of the Japanese Society for Artificial Intelligence, Vol.35, No.4, pp.E-K25_1-17, 2020 (DOI )
- Takahiro Komamizu , “MMEnsemble: Imbalanced Classification Framework Using Metric Learning and Multi-sampling Ratio Ensemble”, DEXA, pp.176-188, 2021 (DOI , slide )
- Takahiro Komamizu , Risa Uehara, Yasuhiro Ogawa, Katsuhiko Toyama, “MUEnsemble: Multi-ratio Undersampling-Based Ensemble Framework for Imbalanced Data”, DEXA, pp.213-228, 2020 (DOI , slide )
- Takahiro Yamakoshi, Takahiro Komamizu , Yasuhiro Ogawa, Katsuhiko Toyama, “Japanese Mistakable Legal Term Correction using Infrequency-aware BERT Classifier”, IEEE BigData, pp.4342-4351, 2019 (DOI )