A Tutorial on Multilabel Learning

被引:549
作者
Gibaja, Eva [1 ]
Ventura, Sebastian [1 ,2 ]
机构
[1] Univ Cordoba, Dept Comp Sci & Numer Anal, E-14071 Cordoba, Spain
[2] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Comp Sci, Jeddah 21413, Saudi Arabia
关键词
Algorithms; Experimentation; Theory; Multilabel learning; ranking; classification; machine learning; data mining; LABEL CLASSIFICATION; DECISION TREES; OBJECT RECOGNITION; FEATURE-SELECTION; ALGORITHMS; FRAMEWORK; MACHINE; STRATEGIES; PREDICTION; RETRIEVAL;
D O I
10.1145/2716262
中图分类号
TP301 [理论、方法];
学科分类号
080201 [机械制造及其自动化];
摘要
Multilabel learning has become a relevant learning paradigm in the past years due to the increasing number of fields where it can be applied and also to the emerging number of techniques that are being developed. This article presents an up-to-date tutorial about multilabel learning that introduces the paradigm and describes the main contributions developed. Evaluation measures, fields of application, trending topics, and resources are also presented.
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页数:38
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