Active learning through density clustering
作者：
Highlights：
• We propose the active learning through density clustering algorithm with three new features.
• We design a new importance measure to select representative instances deterministically.
• We employ tri-partition to determine the action to be taken on each instance.
• The new algorithm generally outperforms state-of-the-art active learning algorithms.
• The new algorithm requires only O(n) of space and O(mn2) of time.
摘要
•We propose the active learning through density clustering algorithm with three new features.•We design a new importance measure to select representative instances deterministically.•We employ tri-partition to determine the action to be taken on each instance.•The new algorithm generally outperforms state-of-the-art active learning algorithms.•The new algorithm requires only O(n) of space and O(mn2) of time.
论文关键词：Active learning,Classification,Density clustering,Master tree,Tri-partitioning
论文评审过程：Received 30 January 2017, Revised 17 May 2017, Accepted 18 May 2017, Available online 18 May 2017, Version of Record 10 June 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.05.046
