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

被引：3
|
作者：
Mylonas, Nikolaos [1 ]
Mollas, Ioannis [1 ]
Liu, Bin [2 ]
Manolopoulos, Yannis [3 ]
Tsoumakas, Grigorios [1 ]
机构：
[1] Aristotle Univ Thessaloniki, Thessaloniki 54124, Greece
[2] Chongqing Univ Posts & Telecommun, Chongqing 400065, Peoples R China
[3] Open Univ Cyprus, CY-2220 Nicosia, Cyprus
来源：
IEEE INTELLIGENT SYSTEMS | 2023年 / 38卷 / 02期
关键词：
Market research; Intelligent systems;
D O I：
10.1109/MIS.2023.3255591
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页码：28 / 31
页数：4
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