تقرير
FAIRification of MLC data
| العنوان: | FAIRification of MLC data |
|---|---|
| المؤلفون: | Kostovska, Ana, Bogatinovski, Jasmin, Treven, Andrej, Džeroski, Sašo, Kocev, Dragi, Panov, Panče |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Computers and Society |
| الوصف: | The multi-label classification (MLC) task has increasingly been receiving interest from the machine learning (ML) community, as evidenced by the growing 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. To FAIRify the MLC datasets, we introduce an ontology-based online catalogue of MLC datasets that follow 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 extensively described in our recent publication in Nature Scientific Reports, Kostovska & Bogatinovski et al., and available at: http://semantichub.ijs.si/MLCdatasetsTest. In addition, we provide an ontology-based system for easy access and querying of performance/benchmark data obtained from a comprehensive MLC benchmark study. The system is available at: http://semantichub.ijs.si/MLCbenchmarkTest. Comment: This paper was accepted ECML PKDD 2022 |
| نوع الوثيقة: | Working Paper |
| الوصول الحر: | http://arxiv.org/abs/2211.12757Test |
| رقم الانضمام: | edsarx.2211.12757 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |