Related references
Note: Only part of the references are listed.Explaining the performance of multilabel classification methods with data set properties
Jasmin Bogatinovski et al.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS (2022)
Comprehensive comparative study of multi-label classification methods
Jasmin Bogatinovski et al.
EXPERT SYSTEMS WITH APPLICATIONS (2022)
The TRUST Principles for digital repositories
Dawei Lin et al.
SCIENTIFIC DATA (2020)
Review of ensembles of multi-label classifiers: Models, experimental study and prospects
Jose M. Moyano et al.
INFORMATION FUSION (2018)
Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository
Francisco Charte et al.
NEUROCOMPUTING (2018)
MLDA: A tool for analyzing multi-label datasets
Jose M. Moyano et al.
KNOWLEDGE-BASED SYSTEMS (2017)
Generic ontology of datatypes
Pance Panov et al.
INFORMATION SCIENCES (2016)
Comment: The FAIR Guiding Principles for scientific data management and stewardship
Mark D. Wilkinson et al.
SCIENTIFIC DATA (2016)
A Tutorial on Multilabel Learning
Eva Gibaja et al.
ACM COMPUTING SURVEYS (2015)
LIFT: Multi-Label Learning with Label-Specific Features
Min-Ling Zhang et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2015)
Ontology of core data mining entities
Pance Panov et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2014)
Acoustic classification of multiple simultaneous bird species: A multi-instance multi-label approach
Forrest Briggs et al.
JOURNAL OF THE ACOUSTICAL SOCIETY OF AMERICA (2012)
An extensive experimental comparison of methods for multi-label learning
Gjorgji Madjarov et al.
PATTERN RECOGNITION (2012)
The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration
Barry Smith et al.
NATURE BIOTECHNOLOGY (2007)