On the Persistence of Multi-Label Learning, its Recent Trends and its Open Issues

N. Mylonas, I. Mollas, B. Liu, Y. Manolopoulos and G. Tsoumakas, "On the Persistence of Multilabel Learning, Its Recent Trends, and Its Open Issues," in IEEE Intelligent Systems, vol. 38, no. 2, pp. 28-31, March-April 2023, doi: 10.1109/MIS.2023.3255591.

Nikolaos Mylonas, Ioannis Mollas, Bin Liu, Yannis Manolopoulos, Grigorios Tsoumakas

Multilabel data comprise instances associated with multiple binary target variables. The main learning task from such data is multilabel classification, where the goal is to output a bipartition of the target variables into relevant and irrelevant ones for a given instance. Other tasks involve ranking the target variables from the most to the least relevant one or even outputting a full joint distribution for every possible assignment of values to the binary targets.