A Novel Model for Imbalanced Data Classification


  • Jian Yin Sun Yat-Sen University
  • Chunjing Gan Sun Yat-Sen University
  • Kaiqi Zhao University of Auckland
  • Xuan Lin Hunan University
  • Zhe Quan Hunan University
  • Zhi-Jie Wang Sun Yat-Sen University




Recently, imbalanced data classification has received much attention due to its wide applications. In the literature, existing researches have attempted to improve the classification performance by considering various factors such as the imbalanced distribution, cost-sensitive learning, data space improvement, and ensemble learning. Nevertheless, most of the existing methods focus on only part of these main aspects/factors. In this work, we propose a novel imbalanced data classification model that considers all these main aspects. To evaluate the performance of our proposed model, we have conducted experiments based on 14 public datasets. The results show that our model outperforms the state-of-the-art methods in terms of recall, G-mean, F-measure and AUC.




How to Cite

Yin, J., Gan, C., Zhao, K., Lin, X., Quan, Z., & Wang, Z.-J. (2020). A Novel Model for Imbalanced Data Classification. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 6680-6687. https://doi.org/10.1609/aaai.v34i04.6145



AAAI Technical Track: Machine Learning