A catalogue with semantic annotations makes multilabel datasets FAIR

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.

Automated summary

Multilabel classification is a machine learning task that aims to label examples with multiple labels simultaneously. It is gaining increasing interest from the machine learning community. Ensuring proper benchmarking is crucial for further development, and this can be achieved by following data management standards such as FAIR and TRUST principles. We introduce an ontology-based online catalogue of multilabel classification datasets, providing comprehensive descriptions and information.