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Anomaly Detection on Natural Language Processing to Improve Predictions on Tourist Preferences
(MDPI, 2022)
[Abstract] Argumentation-based dialogue models have shown to be appropriate for decision contexts in which it is intended to overcome the lack of interaction between decision-makers, either because they are dispersed, they ...
A novel framework for generic Spark workload characterization and similar pattern recognition using machine learning
(Elsevier, 2024-07)
[Abstract]: Comprehensive workload characterization plays a pivotal role in comprehending Spark applications, as it enables the analysis of diverse aspects and behaviors. This understanding is indispensable for devising ...
Fed-mRMR: A lossless federated feature selection method
(Elsevier, 2024-05)
[Abstract]: Feature selection has become a mandatory task in data mining, due to the overwhelming amount of features in Big Data problems. To handle this high-dimensional data and avoid the well-known curse of dimensionality, ...
Reduced precision discretization based on information theory
(Elsevier, 2022-01)
[Abstract] In recent years, new technological areas have emerged and proliferated, such as the Internet of Things or embedded systems in drones, which are usually characterized by making use of devices with strict requirements ...
CUDA-JMI: Acceleration of feature selection on heterogeneous systems
(Elsevier, 2020-01)
[Abstract]: Feature selection is a crucial step nowadays in machine learning and data analytics to remove irrelevant and redundant characteristics and thus to provide fast and reliable analyses. Many research works have ...
Parallel feature selection for distributed-memory clusters
(2019)
[Abstract]: Feature selection is nowadays an extremely important data mining stage in the field of machine learning due to the appearance of problems of high dimensionality. In the literature there are numerous feature ...
Multithreaded and Spark parallelization of feature selection filters
(2016)
[Abstract]: Vast amounts of data are generated every day, constituting a volume that is challenging to analyze. Techniques such as feature selection are advisable when tackling large datasets. Among the tools that provide ...
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 ...
Do all roads lead to Rome? Studying distance measures in the context of machine learning
(Elsevier Ltd, 2023-09)
[Abstract]: Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an ...