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Supervised classification algorithms are used to solve a growing number ofreal-life problems around the globe. Their performance is strictly connectedwith the quality of labels used in training. Unfortunately, acquiringgood-quality annotations for many tasks is infeasible or too expensive to bedone in practice. To tackle this challenge, active learning algorithms arecommonly employed to select only the most relevant data for labeling. However,this is possible only when the quality and quantity of labels acquired fromexperts are sufficient. Unfortunately, in many applications, a trade-offbetween annotating individual samples by multiple annotators to increase labelquality vs. annotating new samples to increase the total number of labeledinstances is necessary. In this paper, we address the issue of faulty dataannotations in the context of active learning. In particular, we propose twonovel annotation unification algorithms that utilize unlabeled parts of thesample space. The proposed methods require little to no intersection betweensamples annotated by different experts. Our experiments on four public datasetsindicate the robustness and superiority of the proposed methods in both, theestimation of the annotator's reliability, and the assignment of actual labels,against the state-of-the-art algorithms and the simple majority voting.

Robust Assignment of Labels for Active Learning with Sparse and Noisy Annotations