Active Learning with Crowdsourcing for the Cold Start of Imbalanced Classifiers [* Cross-Reference *]

The strategy is moreover designed to handle an imbalanced context in which random selection is highly inefficient. In this purpose, our method is guided by an unsupervised clustering, and the computation of cluster quality and impurity indexes, updated at each active learning step.

The strategy is explained on a case study of annotating Twitter content w.r.t. a real flood event. We also show that our technique can cope with multiple heterogeneous data representations. ***This entry has been automatically imported via Infodoc(ASO) CSV by LIST harvest scripts. Please refer to doi.org/10.1007/978-3-030-60816-3_22 for the original and latest version of the dataset and data downloads***