On the Persistence of Multilabel Learning, Its Recent Trends, and Its Open Issues

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

Abstract: 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.