A computational study on 10 benchmark datasets found that MFLRA outperformed eight comparison methods by combining multi-order relevance, label uncertainty and feature interactions.
A new method called Conflict-Aware Posterior Correction softens the hard masking used in partial-label learning, improving ...
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