2026-06-19T20:51:48Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/99432025-01-17T01:05:29Zcom_10259_4219com_10259_5086com_10259_2604col_10259_4220

SSLearn: A Semi-Supervised Learning library for Python Garrido Labrador, José Luis Maudes Raedo, Jesús M. Rodríguez Diez, Juan José García Osorio, César Semi-supervised learning Python library Self-training Co-training Restricted set classification Informática Educación Computer science Education SSLearn is an open-source Python-based library that advances semi-supervised learning (SSL) with a focus on wrapper algorithms and restricted set classification (RSC), a novel paradigm. It fosters innovation by allowing researchers to modify methods or create new ones, facilitating access to state-of-the-art algorithms and comparative studies. As the only library incorporating RSC for constrained classification, SSLearn fills an important gap in SSL tools. Fully compatible with Scikit-Learn, it integrates seamlessly into research workflows, lowering the barrier to entry to SSL and catalyzing its adoption in diverse domains. This makes SSLearn a critical resource for advancing SSL research and applications. This work was supported through the Junta de Castilla 𝑦� León (JCyL) (regional government) under project BU055P20 (JCyL/FEDER, UE), the Spanish Ministry of Science and Innovation under project PID2020- 119894GB-I00 co-financed through European Union FEDER funds, and project TED2021-129485B-C43 funded by MCIN/AEI/ 10.13039/501 100011033 and the European Union NextGenerationEU/PRTR. J.L. Garrido-Labrador is supported through Consejería de Educación of the Junta de Castilla 𝑦� León and the European Social Fund through a pre-doctoral grant EDU/875/2021 (Spain). 2025-01-16T11:19:52Z 2025-01-16T11:19:52Z 2025-01 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 2352-7110 http://hdl.handle.net/10259/9943 10.1016/j.softx.2024.102024 eng SoftwareX. 2025, V. 29, p. 102024 https://doi.org/10.1016/j.softx.2024.102024 Atribución 4.0 Internacional http://creativecommons.org/licenses/by/4.0/ info:eu-repo/semantics/openAccess application/pdf Elsevier