A benchmark and comparison of active learning for logistic regression
作者：
Highlights：
• A review of the state-of-the-art active learning algorithms built on logistic regression is presented, in which links and relationships between methods are explicated.
• A preference map is proposed to reveal characteristic similarities and differences of the selection locations in 2D problems.
• Extensive experiments on 44 real-world datasets and three artificial sets are carried out.
• Insight is provided for the behaviors of classification performance and computational cost.
摘要
•A review of the state-of-the-art active learning algorithms built on logistic regression is presented, in which links and relationships between methods are explicated.•A preference map is proposed to reveal characteristic similarities and differences of the selection locations in 2D problems.•Extensive experiments on 44 real-world datasets and three artificial sets are carried out.•Insight is provided for the behaviors of classification performance and computational cost.
论文关键词：Active learning,Logistic regression,Experimental design,Benchmark,Preference maps
论文评审过程：Received 1 October 2017, Revised 19 April 2018, Accepted 6 June 2018, Available online 22 June 2018, Version of Record 22 June 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.06.004
