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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 ...
A scalable saliency-based feature selection method with instance-level information
(Elsevier, 2019-11)
[Abstract]: Classic feature selection techniques remove irrelevant or redundant features to achieve a subset of relevant features in compact models that are easier to interpret and so improve knowledge extraction. Most ...
Wavefront Marching Methods: A Unified Algorithm to Solve Eikonal and Static Hamilton-Jacobi Equations
(IEEE, 2019-12)
[Abstract]: This paper presents a unified propagation method for dealing with both the classic Eikonal equation, where the motion direction does not affect the propagation, and the more general static Hamilton-Jacobi ...
Anomaly Detection in IoT: Methods, Techniques and Tools
(MDPI AG, 2019-07-22)
[Abstract] Nowadays, the Internet of things (IoT) network, as system of interrelated computing devices with the ability to transfer data over a network, is present in many scenarios of everyday life. Understanding how ...
On developing an automatic threshold applied to feature selection ensembles
(Elsevier, 2019-01)
[Abstract]: Feature selection ensemble methods are a recent approach aiming at adding diversity in sets of selected features, improving performance and obtaining more robust and stable results. However, using an ensemble ...
Distributed correlation-based feature selection in spark
(Elsevier, 2019-09)
[Abstract]: Feature selection (FS) is a key preprocessing step in data mining. CFS (Correlation-Based Feature Selection) is an FS algorithm that has been successfully applied to classification problems in many domains. We ...
A Convolutional Network for Sleep Stages Classification
(2019-02)
[Abstract]: Sleep stages classification is a crucial task in the context of sleep studies. It involves the simultaneous analysis of multiple signals recorded during sleep. However, it is complex and tedious, and even the ...
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 ...
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 ...