On the Persistence of Multilabel Learning, Its Recent Trends, and Its Open Issues
Authors: 
Nikolaos Mylonas, Ioannis Mollas, Bin Liu, 
Yannis Manolopoulos, 
Grigorios TsoumakasAuthors Info & Claims
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.
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This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
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IEEE Educational Activities Department
United States
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Published: 01 March 2023
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