Addressing imbalanced data with argument based rule learning
作者：
Highlights：
• We improve learning rules from imbalanced data by using the expert knowledge.
• An expert explains the decision for some critical examples, giving arguments.
• Three methods of identifying critical examples are proposed and compared.
• Induced rules reflect the expert knowledge and better classify the minority examples.
• Trade-off between the recognition of the minority and majority classes is maintained.
摘要
•We improve learning rules from imbalanced data by using the expert knowledge.•An expert explains the decision for some critical examples, giving arguments.•Three methods of identifying critical examples are proposed and compared.•Induced rules reflect the expert knowledge and better classify the minority examples.•Trade-off between the recognition of the minority and majority classes is maintained.
论文关键词：Imbalanced data,Arguments expressing expert knowledge,Argument based learning,Rule induction,Identification of examples for argumentation
论文评审过程：Received 14 January 2014, Revised 5 May 2015, Accepted 30 July 2015, Available online 7 August 2015, Version of Record 27 September 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.076
