A catalogue with semantic annotations makes multilabel datasets FAIR.
Sci Rep
; 12(1): 7267, 2022 05 04.
Article
em En
| MEDLINE
| ID: mdl-35508507
ABSTRACT
Multilabel classification (MLC) is a machine learning task where the goal is to learn to label an example with multiple labels simultaneously. It receives increasing interest from the machine learning community, as evidenced by the increasing number of papers and methods that appear in the literature. Hence, ensuring proper, correct, robust, and trustworthy benchmarking is of utmost importance for the further development of the field. We believe that this can be achieved by adhering to the recently emerged data management standards, such as the FAIR (Findable, Accessible, Interoperable, and Reusable) and TRUST (Transparency, Responsibility, User focus, Sustainability, and Technology) principles. We introduce an ontology-based online catalogue of MLC datasets originating from various application domains following these principles. The catalogue extensively describes many MLC datasets with comprehensible meta-features, MLC-specific semantic descriptions, and different data provenance information. The MLC data catalogue is available at http//semantichub.ijs.si/MLCdatasets .
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Base de dados:
MEDLINE
Assunto principal:
Semântica
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Aprendizado de Máquina
Idioma:
En
Ano de publicação:
2022
Tipo de documento:
Article