Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets
作者：
Highlights：
• We designed and implemented a novel ensemble based on Class-Switching to deal with the imbalanced class problem.
• The ensemble SwitchingNED changes a fraction of instances of the majority class to the minority class following a new section method based on Nearest Enemy Distance.
• This procedure in combination with traditional data sampling techniques achieves the equilibrium of the class distributions.
• We compare the resulting SwitchingNED with five distinctive ensemble-based approaches. With a better performance, SwitchingNED is settled as one of best approaches on the field.
摘要
•We designed and implemented a novel ensemble based on Class-Switching to deal with the imbalanced class problem.•The ensemble SwitchingNED changes a fraction of instances of the majority class to the minority class following a new section method based on Nearest Enemy Distance.•This procedure in combination with traditional data sampling techniques achieves the equilibrium of the class distributions.•We compare the resulting SwitchingNED with five distinctive ensemble-based approaches. With a better performance, SwitchingNED is settled as one of best approaches on the field.
论文关键词：Imbalanced classification,Ensembles,Preprocessing,Class Switching
论文评审过程：Received 20 October 2016, Revised 4 April 2017, Accepted 28 April 2017, Available online 29 April 2017, Version of Record 9 May 2017.
论文官网地址：https://doi.org/10.1016/j.patcog.2017.04.028
