Automated imbalanced classification via meta-learning
作者:
Highlights:
• The first automated machine learning approach to imbalanced domains.
• 95% reduction of computational efforts concerning workflow selection and optimisation.
• Competitive results in comparison to state-of-the-art AutoML methods.
摘要
•The first automated machine learning approach to imbalanced domains.•95% reduction of computational efforts concerning workflow selection and optimisation.•Competitive results in comparison to state-of-the-art AutoML methods.
论文关键词:Imbalance domain learning,Automated machine learning,Meta-learning,Classification
论文评审过程:Received 31 December 2020, Revised 27 March 2021, Accepted 3 April 2021, Available online 20 April 2021, Version of Record 24 April 2021.
论文官网地址:https://doi.org/10.1016/j.eswa.2021.115011