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dc.contributor.authorPrieta Pintado, Fernando de la 
dc.contributor.authorGil González, Ana Belén 
dc.contributor.authorRodríguez González, Sara 
dc.contributor.authorNavarro Moreno, Maria A.
dc.contributor.authorLópez Batista, Vivian Félix
dc.identifier.citationSoft Computing Models in Industrial and Environmental Applications, 6th International Conference SOCO 2011 Advances in Intelligent and Soft Computing. Advances in Intelligent and Soft Computing. Volumen 87, pp. 523-531.
dc.identifier.isbn978-3-642-19643-0 (Print) / 978-3-642-19644-7 (Online)
dc.identifier.issn1867-5662 (Print) / 1867-5670 (Online)
dc.description.abstractThe rapid evolution within the context of e-learning is closely linked to international efforts on the standardization of Learning Object (LO), which provides ubiquitous access to multiple and distributed educational resources in many repositories. This article presents a system that enables the recovery and classification of LO and provides individualized help with selecting learning materials to make the most suitable choice among many alternatives. For this classification, it is used a special multi-label data mining designed for the LO ranking tasks. According to each position, the system is responsible for presenting the results to the end user. The learning process is supervised, using two major tasks in supervised learning from multi-label data: multi-label classification and label ranking.
dc.publisherSpringer Science + Business Media
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Unported
dc.subjectComputer Science
dc.titleA System for Multi-label Classification of Learning Objects

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Attribution-NonCommercial-NoDerivs 3.0 Unported
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivs 3.0 Unported