Dynamic ensemble selection for multi-class classification with one-class classifiers
作者：
Highlights：
• Dynamic ensemble selection for multi-class decomposition with one-class classifiers.
• Efficient framework for difficult data with high number of classes.
• Removal of non-competent classifiers from decision making process.
• Threshold-based selection mechanism for further ensemble pruning
• Extensive experimental study backed-up with statistical analysis.
摘要
•Dynamic ensemble selection for multi-class decomposition with one-class classifiers.•Efficient framework for difficult data with high number of classes.•Removal of non-competent classifiers from decision making process.•Threshold-based selection mechanism for further ensemble pruning•Extensive experimental study backed-up with statistical analysis.
论文关键词：Machine learning,Classifier ensemble,One-class classification,Multi-class decomposition,Dynamic classifier selection,Ensemble pruning
论文评审过程：Received 4 October 2017, Revised 19 April 2018, Accepted 13 May 2018, Available online 23 May 2018, Version of Record 23 May 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.05.015
