The Emerging Trends of Multi-Label Learning

TL;DR

This paper surveys the emerging trends and challenges in multi-label learning driven by big data, highlighting advances in extreme classification, deep learning, and the need for systemic analysis of this rapidly evolving field.

Contribution

It provides a comprehensive overview of recent developments, identifies key challenges, and suggests future research directions in multi-label learning for big data applications.

Findings

Growth of extreme multi-label classification techniques

Integration of deep learning to capture label dependencies

Identification of new challenges in big data multi-label learning

Abstract

Exabytes of data are generated daily by humans, leading to the growing need for new efforts in dealing with the grand challenges for multi-label learning brought by big data. For example, extreme multi-label classification is an active and rapidly growing research area that deals with classification tasks with an extremely large number of classes or labels; utilizing massive data with limited supervision to build a multi-label classification model becomes valuable for practical applications, etc. Besides these, there are tremendous efforts on how to harvest the strong learning capability of deep learning to better capture the label dependencies in multi-label learning, which is the key for deep learning to address real-world classification tasks. However, it is noted that there has been a lack of systemic studies that focus explicitly on analyzing the emerging trends and new challenges of multi-label learning in the era of big data. It is imperative to call for a comprehensive survey to fulfill this mission and delineate future research directions and new applications.