Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2716262 | ACM Computing Surveys |
Keywords | Field | DocType |
Algorithms,Experimentation,Theory,Multilabel learning,ranking,classification,machine learning,data mining | Data science,Ranking,Computer science,Artificial intelligence,Machine learning | Journal |
Volume | Issue | ISSN |
47 | 3 | 0360-0300 |
Citations | PageRank | References |
119 | 2.26 | 134 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| E. Gibaja | 1 | 121 | 2.63 |
| S. Ventura | 2 | 825 | 34.87 |