Uncertainty-aware twin support vector machines

作者：

Highlights：

• This paper proposes uncertainty-aware twin support vector machines.

• We derive a theorem which helps us simplify the models.

• The proposed decision rule allows us to classify uncertain samples with Gaussian distributions.

• The experiments have been conducted to demonstrate the effectiveness of the proposed models in handling uncertain data.

摘要

•This paper proposes uncertainty-aware twin support vector machines.•We derive a theorem which helps us simplify the models.•The proposed decision rule allows us to classify uncertain samples with Gaussian distributions.•The experiments have been conducted to demonstrate the effectiveness of the proposed models in handling uncertain data.

论文关键词：Uncertain data,Twin support vector machines,Halfspaces,Kernel functions,Data classification

论文评审过程：Received 5 March 2021, Revised 1 January 2022, Accepted 7 April 2022, Available online 11 April 2022, Version of Record 26 April 2022.

论文官网地址：https://doi.org/10.1016/j.patcog.2022.108706