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Simulating the Role of Norms in Processes of Social Innovation: Three Case Studies
(SimSoc Consortium, 2024-01)
[Absctract]: Norms and values are critical drivers in social innovation processes, such as community projects on sustainable energy. Simulating such processes could help uncover conditions that support these social ...
Distributed classification based on distances between probability distributions in feature space
(Elsevier, 2019-09)
[Abstract]: We consider a distributed framework where training and test samples drawn from the same distribution are available, with the training instances spread across disjoint nodes. In this setting, a novel learning ...
Data-driven predictive maintenance framework for railway systems
(IOS Press, 2023)
[Abstract]: The emergence of the Industry 4.0 trend brings automation and data exchange to industrial manufacturing. Using computational systems and IoT devices allows businesses to collect and deal with vast volumes of ...
Dealing with heterogeneity in the context of distributed feature selection for classification
(Springer, 2021)
[Abstract]: Advances in the information technologies have greatly contributed to the advent of larger datasets. These datasets often come from distributed sites, but even so, their large size usually means they cannot be ...
Community detection and social network analysis based on the Italian wars of the 15th century
(Elsevier, 2020)
[Abstract]: In this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions ...
Fast Distributed kNN Graph Construction Using Auto-tuned Locality-sensitive Hashing
(Association for Computing Machinery, 2020)
[Abstract]: The k-nearest-neighbors (kNN) graph is a popular and powerful data structure that is used in various areas of Data Science, but the high computational cost of obtaining it hinders its use on large datasets. ...
How Important Is Data Quality? Best Classifiers vs Best Features
(Elsevier, 2021)
[Abstract] The task of choosing the appropriate classifier for a given scenario is not an easy-to-solve question. First, there is an increasingly high number of algorithms available belonging to different families. And ...
Ensembles for feature selection: A review and future trends
(Elsevier, 2019)
[Abstract]: Ensemble learning is a prolific field in Machine Learning since it is based on the assumption that combining the output of multiple models is better than using a single model, and it usually provides good ...
Large scale anomaly detection in mixed numerical and categorical input spaces
(Elsevier, 2019)
[Abstract]: This work presents the ADMNC method, designed to tackle anomaly detection for large-scale problems with a mixture of categorical and numerical input variables. A flexible parametric probability measure is ...
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 ...