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

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.

References in corpus (6)

• Deep Learning in Neural Networks: An Overview
• Natural Language Processing (almost) from Scratch
• CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning
• Diagnose like a Radiologist: Attention Guided Convolutional Neural Network for Thorax Disease Classification
• LSHTC: A Benchmark for Large-Scale Text Classification
• Comparison of State-of-the-Art Deep Learning APIs for Image Multi-Label Classification using Semantic Metrics