UniMLR uses positive-positive label pairs and Gaussian significance scores to unify multi-label classification and ranking, evaluated on new Ranked MNIST datasets.
Multi-label Ranking: Mining Multi-label and Label Ranking Data
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abstract
We survey multi-label ranking tasks, specifically multi-label classification and label ranking classification. We highlight the unique challenges, and re-categorize the methods, as they no longer fit into the traditional categories of transformation and adaptation. We survey developments in the last demi-decade, with a special focus on state-of-the-art methods in deep learning multi-label mining, extreme multi-label classification and label ranking. We conclude by offering a few future research directions.
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UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking
UniMLR uses positive-positive label pairs and Gaussian significance scores to unify multi-label classification and ranking, evaluated on new Ranked MNIST datasets.