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Mostrando ítems 11-20 de 30
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 ...
E2E-FS: An End-to-End Feature Selection Method for Neural Networks
(IEEE, 2023-07)
[Abstract]: Classic embedded feature selection algorithms are often divided in two large groups: tree-based algorithms and LASSO variants. Both approaches are focused in different aspects: while the tree-based algorithms ...
Fast anomaly detection with locality-sensitive hashing and hyperparameter autotuning
(Elsevier, 2022-08)
[Abstract]: This paper presents LSHAD, an anomaly detection (AD) method based on Locality Sensitive Hashing (LSH), capable of dealing with large-scale datasets. The resulting algorithm is highly parallelizable and its ...
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 ...
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 ...
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 ...
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 ...
An Agent-Based Model to Simulate the Spread of a Virus Based on Social Behavior and Containment Measures
(MDPI AG, 2020-08-20)
[Abstract]
COVID-19 has brought a new normality in society. However, to avoid the situation, the virus must be stopped. There are several ways in which the governments of the world have taken action, from small measures ...
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 ...