A Systematic Literature Review on Multi-label Data Stream Classification
It synthesizes existing work for researchers in machine learning and data mining, but is incremental as it reviews rather than proposes new methods.
This systematic literature review analyzes multi-label data stream classification methods, characterizing their approaches to challenges like concept drift and concept evolution, and identifies gaps and future research directions.
Classification in the context of multi-label data streams represents a challenge that has attracted significant attention due to its high real-world applicability. However, this task faces problems inherent to dynamic environments, such as the continuous arrival of data at high speed and volume, changes in the data distribution (concept drift), the emergence of new labels (concept evolution), and the latency in the arrival of ground truth labels. This systematic literature review presents an in-depth analysis of multi-label data stream classification proposals. We characterize the latest methods in the literature, providing a comprehensive overview, building a thorough hierarchy, and discussing how the proposals approach each problem. Furthermore, we discuss the adopted evaluation strategies and analyze the methods' asymptotic complexity and resource consumption. Finally, we identify the main gaps and offer recommendations for future research directions in the field.