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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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
A review of green artificial intelligence: Towards a more sustainable future
(Elsevier B.V., 2024-09-28)
[Abstract]: Green artificial intelligence (AI) is more environmentally friendly and inclusive than conventional AI, as it not only produces accurate results without increasing the computational cost but also ensures that ...
Do all roads lead to Rome? Studying distance measures in the context of machine learning
(Elsevier Ltd, 2023-09)
[Abstract]: Many machine learning and data mining tasks are based on distance measures, so a large amount of literature addresses this aspect somehow. Due to the broad scope of the topic, this paper aims to provide an ...
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 ...
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 ...