Active semi-supervised learning with multiple complementary information
作者：
Highlights：
• We propose an active semi-supervised learning algorithm with multiple criteria.
• The criteria are representativeness, diversity, and variance reduction of a model.
• We use clustering information to develop representativeness and diversity criteria.
• The proposed algorithm is useful to avoid selection of undesirable samples.
摘要
•We propose an active semi-supervised learning algorithm with multiple criteria.•The criteria are representativeness, diversity, and variance reduction of a model.•We use clustering information to develop representativeness and diversity criteria.•The proposed algorithm is useful to avoid selection of undesirable samples.
论文关键词：Active learning,Optimal experimental design,Semi-supervised learning,Representativeness,Diversity
论文评审过程：Received 11 April 2018, Revised 1 February 2019, Accepted 12 February 2019, Available online 13 February 2019, Version of Record 18 February 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.02.017
