BAdaCost: Multi-class Boosting with Costs

作者：

Highlights：

• Boosting algorithm for solving multi-class cost-sensitive problems.

• The cost matrix can be used as a tool to learn class boundaries (i.e class imbalance, detection problems, Jaccard or F1 objective problems, etc).

• Multi-class object detectors can be developed with BAdaCost resulting in faster performance than the one-vs-background usual approach (using binary detectors).

• The cost matrix can be used to improve Average Precision (AP) in multi-view object detection problems.

摘要

•Boosting algorithm for solving multi-class cost-sensitive problems.•The cost matrix can be used as a tool to learn class boundaries (i.e class imbalance, detection problems, Jaccard or F1 objective problems, etc).•Multi-class object detectors can be developed with BAdaCost resulting in faster performance than the one-vs-background usual approach (using binary detectors).•The cost matrix can be used to improve Average Precision (AP) in multi-view object detection problems.

论文关键词：Boosting,Multi-class classification,Cost-sensitive classification,Multi-view object detection

论文评审过程：Received 8 August 2017, Revised 21 December 2017, Accepted 18 February 2018, Available online 20 February 2018, Version of Record 5 March 2018.

论文官网地址：https://doi.org/10.1016/j.patcog.2018.02.022