ListarGI-LIDIA - Artigos por tema "Feature selection"
Mostrando ítems 1-18 de 18
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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 ... -
CUDA acceleration of MI-based feature selection methods
(Elsevier, 2024-08)[Abstract]: Feature selection algorithms are necessary nowadays for machine learning as they are capable of removing irrelevant and redundant information to reduce the dimensionality of the data and improve the quality of ... -
Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry
(Elsevier Ltd, 2019-06)[Abstract]: Proactive Maintenance practices are becoming more standard in industrial environments, with a direct and profound impact on the competitivity within the sector. These practices demand the continuous monitorization ... -
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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
Feature selection with limited bit depth mutual information for portable embedded systems
(Elsevier, 2020-06)[Abstract]: Since wearable computing systems have grown in importance in the last years, there is an increased interest in implementing machine learning algorithms with reduced precision parameters/computations. Not only ... -
Finding a needle in a haystack: insights on feature selection for classification tasks
(Springer, 2024-04)[Abstract]: The growth of Big Data has resulted in an overwhelming increase in the volume of data available, including the number of features. Feature selection, the process of selecting relevant features and discarding ... -
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 ... -
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 ... -
Low-Precision Feature Selection on Microarray Data: An Information Theoretic Approach
(Springer, 2022)[Abstract] The number of interconnected devices, such as personal wearables, cars, and smart-homes, surrounding us every day has recently increased. The Internet of Things devices monitor many processes, and have the ... -
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 ... -
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 ... -
On the scalability of feature selection methods on high-dimensional data
(Springer, 2018)[Abstract]: Lately, derived from the explosion of high dimensionality, researchers in machine learning became interested not only in accuracy, but also in scalability. Although scalability of learning methods is a trending ... -
Scalable Feature Selection Using ReliefF Aided by Locality-Sensitive Hashing
(Wiley, 2021)[Abstract] Feature selection algorithms, such as ReliefF, are very important for processing high-dimensionality data sets. However, widespread use of popular and effective such algorithms is limited by their computational ... -
Towards federated feature selection: Logarithmic division for resource-conscious methods
(Elsevier, 2024)[Abstract]: Feature selection is a popular preprocessing step to reduce the dimensionality of the data while preserving the important information. In this paper, we propose an efficient and green feature selection method ...
