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dc.contributor.authorMorales Rodríguez, David
dc.contributor.authorPegalajar Cuéllar, Manuel 
dc.contributor.authorMorales Santos, Diego Pedro
dc.identifier.citationRodríguez, D.M., Cuéllar, M.P. & Morales, D.P. Concept logic trees: enabling user interaction for transparent image classification and human-in-the-loop learning. Appl Intell 54, 3667–3679 (2024).
dc.description.abstractInterpretable deep learning models are increasingly important in domains where transparent decision-making is required. In this field, the interaction of the user with themodel can contribute to the interpretability of themodel. In this research work, we present an innovative approach that combines soft decision trees, neural symbolic learning, and concept learning to create an image classificationmodel that enhances interpretability and user interaction, control, and intervention. The key novelty of our method relies on the fusion of an interpretable architecture with neural symbolic learning, allowing the incorporation of expert knowledge and user interaction. Furthermore, our solution facilitates the inspection of the model through queries in the form of first-order logic predicates. Our main contribution is a human-in-the-loop model as a result of the fusion of neural symbolic learning and an interpretable architecture.We validate the effectiveness of our approach through comprehensive experimental results, demonstrating competitive performance on challenging datasets when compared to state-of-the-art solutions.es_ES
dc.description.sponsorshipHAT.tec GmbHes_ES
dc.description.sponsorshipFunding for open access publishing: Universidad de Granada/CBUA.es_ES
dc.publisherSpringer Naturees_ES
dc.rightsAtribución 4.0 Internacional*
dc.subjectSoft decision treeses_ES
dc.titleConcept logic trees: enabling user interaction for transparent image classification and human-in-the-loop learninges_ES

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Atribución 4.0 Internacional
Except where otherwise noted, this item's license is described as Atribución 4.0 Internacional