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

页码：28 / 31

页数：4

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