Partial multi-label learning via three-way decision-based tri-training

In real-world application scenarios, multi-label learning (MLL) datasets often contain some irrelevant noisy labels, which degrades the performance of traditional multi-label learning models. In order to deal with this problem, partial multi-label learning (PML) is proposed, in which each instance is associated with a candidate label set, which includes multiple relevant ground-truth labels and some irrelevant noisy labels. The common strategy to deal with this problem is disambiguating the candidate label set, but the co-occurrence of noisy labels and ground-truth labels makes the disambiguation technique susceptible to error. In this paper, a novel disambiguation-free PML approach named PMLTT is proposed. Specifically, by adapting the tri-training framework, mutual cooperation and iteration between classifiers are used to correct noisy labels and improve the performance of the learning model. Moreover, the three-way decision is adapted to solve the conflict problem of the base classifier and obtain more useful training samples. In addition, the precise supervisory information of the noncandidate labels is exploited to make the predictions of the base classifier more accurate. Finally, experimental results on both synthetic and real-world PML datasets show that the proposed PML-TT approach can effectively reduce the negative influence of noisy labels and learn a robust model.& COPY; 2023 Elsevier B.V. All rights reserved.

Automated summary

In this paper, a novel disambiguation-free PML approach named PMLTT is proposed, which uses mutual cooperation and iteration between classifiers to correct noisy labels and improve the performance of the learning model. The three-way decision and precise supervisory information are also utilized to solve conflicts and make predictions more accurate. Experimental results show that the proposed approach effectively reduces the negative influence of noisy labels and learns a robust model.