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Multi-label Ranking: Mining Multi-label and Label Ranking Data

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arxiv 2101.00583 v1 pith:CXNHFWAI submitted 2021-01-03 cs.LG

Lihi Dery This is my paper
classification cs.LG
keywords multi-labelrankingclassificationlabelmethodsminingsurveyadaptation
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. UniMLR: Modeling Implicit Class Significance for Multi-Label Ranking

    cs.LG 2025-08 conditional novelty 4.0 of 10

    UniMLR uses positive-positive label pairs and Gaussian significance scores to unify multi-label classification and ranking, evaluated on new Ranked MNIST datasets.

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