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Feature Selection With Limited Bit Depth Mutual Information for Embedded Systems
(MDPI AG, 2018-09-17)
[Abstract] Data is growing at an unprecedented pace. With the variety, speed and volume of data flowing through networks and databases, newer approaches based on machine learning are required. But what is really big in Big ...
Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings
(2019)
[Abstract] The aim of this work is to propose different statistical and machine learning methodologies for identifying
anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings. ...
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 ...
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 ...
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 ...
Feature selection for domain adaptation using complexity measures and swarm intelligence
(Elsevier B.V., 2023-09-01)
[Abstract]: Particle Swarm Optimization is an optimization algorithm that mimics the behaviour of a flock of birds, setting multiple particles that explore the search space guided by a fitness function in order to find the ...
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 ...
Insights into distributed feature ranking
(Elsevier, 2019)
[Abstract]: In an era in which the volume and complexity of datasets is continuously growing, feature selection techniques have become indispensable to extract useful information from huge amounts of data. However, existing ...
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 ...
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 ...
