In crowdsourcing learning

a certain level of label noise still exists in integrated labels obtained by employing ground truth inference algorithms. Inspired by the tri-training idea

this paper proposes a tri-training-based label noise correction (TTLNC) algorithm for crowdsourcing. TTLNC at first employs a filter to get a clean set and a noisy set and then trains three different classifiers from the bagged clean set. Furthermore

each instance from the noisy set is relabeled by these classifiers and assigned to the corresponding training set according to the designed instance assignment strategy. Finally

three classifiers are retrained on three new training sets and are used to relabel all instances. Experimental results on both simulated benchmark data and real-world crowdsourced data show that TTLNC significantly outperforms other four state-of-the-art noise correction algorithms in team of the noise ratio and the model quality.