Label Confidence Based Ensemble Partial Label Learning

Partial Label Learning (PLL) aims to train a multi-class classifier to deal with the problem that each training instance is associated with multiple candidate labels, but only one of them is true. For PLL, the averaging-based disambiguation strategy treats all candidate labels equally, however, this strategy may lead to the model prediction of candidate labels being stronger than the model prediction of valid labels. In this paper, decision trees are retrofitted by considering the inherent properties of PLL, and a method named PL-RF (label confidence based ensemble partial label learning) is proposed that can overcome the defects of averaging-based disambiguation strategies and obtain better classification performance by ensembleing multiple partial label decision trees, then determining the final predicted label by voting on each tree. Extensive experiments over both real-world and synthetic partial label data sets empirically justify the effectiveness of PL-RF, which achieves more competitive performance than other state-of-the-art PLL methods.