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Explicabilidad Sostenible para Sistemas de Recomendación mediante Ranking Bayesiano de Imágenes
(AEPIA, 2024)
[Abstract]: Los Sistemas de Recomendacion se han vuelto cruciales por su gran influencia en la sociedad pero, siendo mayoritariamente sistemas de caja negra, fomentar su transparencia es tan primordial como complejo; ...
Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing
(Wiley, 2021)
[Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational ...
Aprendizaje automático para combatir la toxicidad en conversaciones sobre salud en línea
(AEPIA, 2024)
[Abstract]: En temas relacionados con la salud publica, la toxicidad de usuarios en conversaciones en redes sociales puede ser una fuente de conflicto social o promover comportamientos peligrosos sin base científica. Los ...
Adaptive Real-Time Method for Anomaly Detection Using Machine Learning
(MDPI AG, 2020-08-20)
[Abstract]
Anomaly detection is a sub-area of machine learning that deals with the development of methods to distinguish among normal and anomalous data. Due to the frequent use of anomaly-detection systems in monitoring ...
A Machine Learning Solution for Distributed Environments and Edge Computing
(MDPI AG, 2019-08-09)
[Abstract] In a society in which information is a cornerstone the exploding of data is crucial. Thinking of the Internet of Things, we need systems able to learn from massive data and, at the same time, being inexpensive ...
Sustainable personalisation and explainability in Dyadic Data Systems
(2022)
[Abstract]: Systems that rely on dyadic data, which relate entities of two types together, have become ubiquitously used in fields such as media services, tourism business, e-commerce, and others. However, these systems ...
DSVD-autoencoder: A scalable distributed privacy-preserving method for one-class classification
(John Wiley and Sons Ltd, 2021-01)
[Abstract]: One-class classification has gained interest as a solution to certain kinds of problems typical in a wide variety of real environments like anomaly or novelty detection. Autoencoder is the type of neural network ...
Interpretable market segmentation on high dimension data
(M D P I AG, 2018-09-17)
[Abstract] Obtaining relevant information from the vast amount of data generated by interactions in a market or, in general, from a dyadic dataset, is a broad problem of great interest both for industry and academia. Also, ...
A One-Class Classification method based on Expanded Non-Convex Hulls
(Elsevier, 2023)
[Abstract]: This paper presents an intuitive, robust and efficient One-Class Classification algorithm. The method developed is called OCENCH (One-class Classification via Expanded Non-Convex Hulls) and bases its operation ...
Machine Learning Techniques to Predict Different Levels of Hospital Care of CoVid-19
(Springer, 2022)
[Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital ...
