Multi-label Ranking: Mining Multi-label and Label Ranking Data
This survey provides a structured overview and re-categorization of multi-label ranking methods, which is useful for researchers and practitioners working with complex classification problems.
This paper surveys multi-label ranking tasks, including multi-label classification and label ranking classification. It re-categorizes existing methods and reviews state-of-the-art deep learning, extreme multi-label classification, and label ranking techniques from the last five years.
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.