A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
The task of obtaining meaningful annotations is a tedious work, incurring considerable costs and time consumption. Dynamic active learning and cooperative learning are recently proposed approaches to reduce human effort of annotating data with subjective phenomena. In this paper, we introduce a novel generic annotation framework, with the aim to achieve the optimal tradeoff between label reliability and cost reduction by making efficient use of human and machine work force. To this end, we usedoi:10.1109/tcyb.2019.2901499 pmid:30872254 fatcat:hbbmbw3kgvhztln5soziwv2mla