Siamese networks with an online reweighted example for imbalanced data learning
作者：
Highlights：
• A unified approach that combines few-shot learning and reweighted example learning called ORESCNN is used for classifying unbalanced datasets.
• An ORE algorithm is introduced to handle the problem of data with a class-imbalanced distribution, which alleviates the influence of imbalanced data on model performance by reweighted example learning.
• Extensive experiments are conducted on experimental databases, and the results show that the proposed approach favourably performs against baseline methods.
摘要
•A unified approach that combines few-shot learning and reweighted example learning called ORESCNN is used for classifying unbalanced datasets.•An ORE algorithm is introduced to handle the problem of data with a class-imbalanced distribution, which alleviates the influence of imbalanced data on model performance by reweighted example learning.•Extensive experiments are conducted on experimental databases, and the results show that the proposed approach favourably performs against baseline methods.
论文关键词：Few-shot learning,Reweighted example learning,Data mining,Imbalanced learning
论文评审过程：Received 9 January 2020, Revised 26 May 2022, Accepted 25 July 2022, Available online 26 July 2022, Version of Record 4 August 2022.
论文官网地址：https://doi.org/10.1016/j.patcog.2022.108947
