4.7 Article

A Tutorial on Multilabel Learning

期刊

ACM COMPUTING SURVEYS
卷 47, 期 3, 页码 -

出版社

ASSOC COMPUTING MACHINERY
DOI: 10.1145/2716262

关键词

Algorithms; Experimentation; Theory; Multilabel learning; ranking; classification; machine learning; data mining

资金

  1. Ministry of Science and Technology project [TIN-2011-22408]

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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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