Progressive boosting for class imbalance and its application to face re-identification
作者：
Highlights：
• The progressive Boosting Ensemble is proposed for learning from imbalanced data.
• Partitioning data in Boosting lead to higher diversity and less information loss.
• Trajectory under-sampling in PBoost is more effective for face re-identification.
• Validating on various skew levels of data in Boosting increases robustness to skew.
• Partitioning and validating on different skew levels reduce computation complexity.
摘要
•The progressive Boosting Ensemble is proposed for learning from imbalanced data.•Partitioning data in Boosting lead to higher diversity and less information loss.•Trajectory under-sampling in PBoost is more effective for face re-identification.•Validating on various skew levels of data in Boosting increases robustness to skew.•Partitioning and validating on different skew levels reduce computation complexity.
论文关键词：Class imbalance,Ensemble learning,Boosting,Face re-identification,Video surveillance
论文评审过程：Received 5 September 2017, Revised 15 January 2018, Accepted 16 January 2018, Available online 31 January 2018, Version of Record 12 March 2018.
论文官网地址：https://doi.org/10.1016/j.eswa.2018.01.023
