We present an open-source toolkit that allows the easy comparison of the performance of active learning methods over a series of datasets. The toolkit allows such strategies to be constructed by combining a judgement aggregation model, task selection method and worker selection method. The toolkit also provides a user interface which allows researchers to gain insight into worker performance and task classification at runtime.
Citation
Venanzi, Matteo, Parson, Oliver, Rogers, Alex and Jennings, Nicholas R. (2015) The ActiveCrowdToolkit: An Open-Source Tool for Benchmarking Active Learning Algorithms for Crowdsourcing Research At The Third AAAI Conference on Human Computation and Crowdsourcing (HCOMP-2015).
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