Machine learning with crowdsourcing: A brief summary of the past research and future directions
With crowdsourcing systems, labels can be obtained with low cost, which facilitates the
creation of training sets for prediction model learning. However, the labels obtained from …
creation of training sets for prediction model learning. However, the labels obtained from …
Knowledge learning with crowdsourcing: A brief review and systematic perspective
J Zhang - IEEE/CAA Journal of Automatica Sinica, 2022 - ieeexplore.ieee.org
Big data have the characteristics of enormous volume, high velocity, diversity, value-sparsity,
and uncertainty, which lead the knowledge learning from them full of challenges. With the …
and uncertainty, which lead the knowledge learning from them full of challenges. With the …
Crowdsourcing last mile delivery: strategic implications and future research directions
The rise of e‐commerce over the past 20 years has created an increased need for
responsive omnichannel distribution to meet the last mile challenge. Some companies are …
responsive omnichannel distribution to meet the last mile challenge. Some companies are …
Iterative learning for reliable crowdsourcing systems
Crowdsourcing systems, in which tasks are electronically distributed to
numerous``information piece-workers'', have emerged as an effective paradigm for human …
numerous``information piece-workers'', have emerged as an effective paradigm for human …
Budget-optimal task allocation for reliable crowdsourcing systems
Crowdsourcing systems, in which numerous tasks are electronically distributed to numerous
“information pieceworkers,” have emerged as an effective paradigm for human-powered …
“information pieceworkers,” have emerged as an effective paradigm for human-powered …
Shepherding the crowd yields better work
Micro-task platforms provide massively parallel, on-demand labor. However, it can be
difficult to reliably achieve high-quality work because online workers may behave …
difficult to reliably achieve high-quality work because online workers may behave …
Learning from crowdsourced labeled data: a survey
With the rapid growing of crowdsourcing systems, quite a few applications based on a
supervised learning paradigm can easily obtain massive labeled data at a relatively low …
supervised learning paradigm can easily obtain massive labeled data at a relatively low …
Coupled confusion correction: Learning from crowds with sparse annotations
As the size of the datasets getting larger, accurately annotating such datasets is becoming
more impractical due to the expensiveness on both time and economy. Therefore, crowd …
more impractical due to the expensiveness on both time and economy. Therefore, crowd …
Designing technology for on‐demand delivery: The effect of customer tipping on crowdsourced driver behavior and last mile performance
Crowdsourcing technology platforms specializing in on‐demand last mile delivery face a
novel problem—heightened agent independence increases uncertainty in last mile delivery …
novel problem—heightened agent independence increases uncertainty in last mile delivery …
Efficient crowdsourcing for multi-class labeling
Crowdsourcing systems like Amazon's Mechanical Turk have emerged as an effective large-
scale human-powered platform for performing tasks in domains such as image classification …
scale human-powered platform for performing tasks in domains such as image classification …