Imbalanced Classification

+ + +

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.

Publications

International Journals

  • Takahiro Komamizu, “Combining Multi-ratio Undersampling and Metric Learning for Imbalanced Classification”, JDI, Vol.2, No.4, pp.462-475, 2021 (DOI)
  • 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)

International Conferences

  • 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)

Japanese Domestic Conferences

  • 植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦, “弱分類器の調整に基づく不均衡データ向けアンサンブル・フレームワーク”, 第12回Webとデータベースに関するフォーラム, pp.81-84, 2019 (link) – MicroAd Award , FRONTEO Award , FUJITSU Award
  • 植原 リサ, 駒水 孝裕, 小川 泰弘, 外山 勝彦, “不均衡データ分類フレームワークにおけるサンプリング比率の最適化”, 第12回データ工学と情報マネジメントに関するフォーラム, pp.F8-2, 2020 – Online Presentation Award