Adaptive cost-sensitive learning: Improving the convergence of intelligent diagnosis models under imbalanced data

作者：

Highlights：

• A novel cost-adaptive calculation method is proposed.

• The cost varies adaptively with the sample distribution, the sample convergence trend, and the category convergence trend.

• By applying the sample cost to the loss function, the losses of each class are coordinated.

• Results with different models, different loss functions, and different datasets show the flexibility of the proposed method.

摘要

•A novel cost-adaptive calculation method is proposed.•The cost varies adaptively with the sample distribution, the sample convergence trend, and the category convergence trend.•By applying the sample cost to the loss function, the losses of each class are coordinated.•Results with different models, different loss functions, and different datasets show the flexibility of the proposed method.

论文关键词：Intelligent fault diagnosis,Imbalanced data,Imbalanced learning,Cost-sensitive learning,Adaptive cost

论文评审过程：Received 24 October 2021, Revised 20 January 2022, Accepted 22 January 2022, Available online 31 January 2022, Version of Record 12 February 2022.

论文官网地址：https://doi.org/10.1016/j.knosys.2022.108296