An active learning paradigm based on a priori data reduction and organization

作者：

Highlights：

• A novel active learning paradigm, called DROP, based on a priori data reduction and organization.

• DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.

• The proposed paradigm allows to achieve high accuracy quickly with minimum user interaction.

• Results are shown with different clustering and classification strategies, and on a variety of real-world datasets.

摘要

•A novel active learning paradigm, called DROP, based on a priori data reduction and organization.•DROP does not require classification and reorganization of all non-annotated samples in the dataset at each iteration.•The proposed paradigm allows to achieve high accuracy quickly with minimum user interaction.•Results are shown with different clustering and classification strategies, and on a variety of real-world datasets.

论文关键词：Pattern recognition,Active learning,Image annotation,Machine learning,Data mining

论文评审过程：Available online 18 April 2014.

论文官网地址：https://doi.org/10.1016/j.eswa.2014.04.007