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

TL;DR

This paper surveys recent advances in multi-label ranking, focusing on deep learning, extreme classification, and label ranking, highlighting challenges, new categorizations, and future research directions.

Contribution

It re-categorizes multi-label ranking methods beyond traditional transformation and adaptation, emphasizing recent developments in deep learning and extreme classification.

Findings

Deep learning methods have advanced multi-label mining.

Extreme multi-label classification addresses large label spaces.

The survey identifies key challenges and future research directions.

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.