The Emerging Trends of Multi-Label Learning
This paper highlights a lack of comprehensive analysis for researchers and practitioners working with multi-label learning in the context of big data, proposing a survey to fill this gap.
This paper identifies a gap in systemic studies analyzing emerging trends and challenges in multi-label learning within the big data era. It advocates for a comprehensive survey to address this need and outline future research directions and applications.
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.