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A comparison of performance of K-complex classification methods using feature selection
(2016-01-20)
[Abstract] The main objective of this work is to obtain a method that achieves the best accuracy results with a low false positive rate in the classification of K-complexes, a kind of transient waveform found in the ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...