In partial multi-label learning (PML), each training example is associated with multiple candidate labels which are only partially valid. The task of PML naturally arises in learning scenarios with inaccurate supervision, and the goal is to induce a multi-label predictor which can assign a set of proper labels for unseen instance. To learn from PML training examples, the training procedure is prone to be misled by the false positive labels concealed in candidate label set. In light of this major difficulty, a novel two-stage PML approach is proposed which works by eliciting credible labels from the candidate label set for model induction. In this way, most false positive labels are expected to be excluded from the training procedure. Specifically, in the first stage, the labeling confidence of candidate label for each PML training example is estimated via iterative label propagation. In the second stage, by utilizing credible labels with high labeling confidence, multi-label predictor is induced via pairwise label ranking with virtual label splitting or maximum a posteriori (MAP) reasoning. Extensive experiments on synthetic as well as real-world data sets clearly validate the effectiveness of credible label elicitation in learning from PML examples.
To mitigate the negative impact of irrelevant labels in the training phase, REF pro-posed a two-stage approach (PARTICLE), which firstly elicits credible labels via iterative label propagation, and then takes the elicited labels to induce a multi-label classifier with virtual label splitting (PARTICLE-VLS) or maximum a posteriori reasoning (PARTICLE-MAP).
Partial Multi-Label Learning via Credible Label Elicitation
AAAI
Computer Science