Local and global feature selection for multilabel classification with binary relevance : An empirical comparison on flat and hierarchical problems


Melo, André ; Paulheim, Heiko


DOI: https://doi.org/10.1007/s10462-017-9556-4
URL: http://link.springer.com/article/10.1007/s10462-01...
Additional URL: http://www.heikopaulheim.com/docs/aireview2017.pdf
Document Type: Article
Year of publication: 2019
The title of a journal, publication series: Artificial Intelligence Review
Volume: 51
Issue number: 1
Page range: 33-60
Place of publication: Dordrecht [u.a.]
Publishing house: Springer Science + Business Media B.V.
ISSN: 0269-2821 , 1573-7462
Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik V (Bizer)
Subject: 004 Computer science, internet

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Melo, André ; Paulheim, Heiko ORCID: 0000-0003-4386-8195 (2019) Local and global feature selection for multilabel classification with binary relevance : An empirical comparison on flat and hierarchical problems. Artificial Intelligence Review Dordrecht [u.a.] 51 1 33-60 [Article]


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