Review on Multi-lable Classification

Abstract

Multi-label classification refers to the classification problem where multiple labels may coexist in a single sample. It has been widely applied in fields such as text classification, image classification, music and video classification. Unlike traditional single-label classification problems, multi-label classification problems become more complex due to the possible correlation or dependence among labels. In recent years, with the rapid development of deep learning technology, many multi-label classification methods combined with deep learning have gradually become a research hotspot. Therefore, this paper summarizes the multi-label classification methods from the traditional and deep learning-based perspectives, and analyzes the key ideas, representative models, and advantages and disadvantages of each method. In traditional multi-label classification methods, problem transformation methods and algorithm adaptation methods are introduced. In deep learning-based multi-label classification methods, the latest multi-label classification methods based on Transformer are reviewed particularly, which have become one of the mainstream methods to solve multi-label classification problems. Additionally, various multi-label classification datasets from different domains are introduced, and 15 evaluation metrics for multi-label classification are briefly analyzed. Finally, future work is discussed from the perspectives of multi-modal data multi-label classification, prompt learning-based multi-label classification, and imbalanced data multi-label classification, in order to further promote the development and application of multi-label classification.

Keywords

• multi-label classification; problem transformation; algorithm adaptation; deep learning