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Feature Selection in Big Image Datasets
(MDPI AG, 2020-08-24)
[Abstract]
In computer vision, current feature extraction techniques generate high dimensional data. Both convolutional neural networks and traditional approaches like keypoint detectors are used as extractors of high-level ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...