Active Learning-Based Crowd Replication

<p>Crowd replication, which combines crowd sensing, direct observation, and mathematical modeling to enable efficient and accurate evaluation of crowd, is a low-effort, easy-to-adopt and cost-effective mechanism for crowd data collection and analysis. In crowd replication, the quality of data collection is particularly important, therefore, a novel method of data collection is proposed. We apply active learning, which is a modern method in machine learning, aiming to reduce the sample size, complexity, and increase the accuracy of the data tasks as much as possible with less data, to allow us to obtain the more informative dataset. We demonstrate with experimental results that, compared with the traditional probability-based method, our contributions enable stably capturing a more representative dataset.</p>