Exploring the combination of self and mutual teaching for tabular-data-related semi-supervised regression
作者：
Highlights：
• Data augmentation tricks may be unapplicable for tabular data.
• The combination of self and mutual teaching for SSL regression tasks is explored.
• Self teaching improves performance by enhancing robustness and stability.
• Mutual teaching provides help by avoiding confirmation bias.
• Experiments verify the effectiveness of the method.
摘要
•Data augmentation tricks may be unapplicable for tabular data.•The combination of self and mutual teaching for SSL regression tasks is explored.•Self teaching improves performance by enhancing robustness and stability.•Mutual teaching provides help by avoiding confirmation bias.•Experiments verify the effectiveness of the method.
论文关键词：Semi-supervised regression,Self teaching,Mutual teaching,Tabular data
论文评审过程：Received 17 February 2022, Revised 7 September 2022, Accepted 25 September 2022, Available online 30 September 2022, Version of Record 12 October 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.118931
