Laura Moran-Fernandez

Expert Syst. J. Knowl. Eng., March, 2025

ProgTutor: A Robotic-Based Framework to Support Teaching and Learning of Programming Fundamentals.

[BibT_eX]

[DOI]

Javier Ortega-Morla

Antonio Leis

Alma Mallo

Alejandro Rodríguez-Arias

Sara Guerreiro-Santalla

Alejandro Paz-Lopez

Beatriz Pérez-Sánchez

Noelia Sánchez-Maroño

Oscar Fontenla-Romero

Francisco Bellas

IEEE Trans. Learn. Technol., 2025

Breaking boundaries: Low-precision conditional mutual information for efficient feature selection.

[BibT_eX]

[DOI]

Eva Blanco-Mallo

Konstantinos Sechidis

Pattern Recognit., 2025

Efficient ReliefF: A Low-Power Optimization of ReliefF for Resource-Constrained Devices.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2025, 2025

2024

Finding a needle in a haystack: insights on feature selection for classification tasks.

[BibT_eX]

[DOI]

J. Intell. Inf. Syst., April, 2024

CUDA acceleration of MI-based feature selection methods.

[BibT_eX]

[DOI]

Bieito Beceiro

Jorge González-Domínguez

Juan Touriño

J. Parallel Distributed Comput., 2024

A review of green artificial intelligence: Towards a more sustainable future.

[BibT_eX]

[DOI]

Brais Cancela

Neurocomputing, 2024

Mutual Information-Based Feature Selection for Federated Learning Environments.

[BibT_eX]

[DOI]

Proceedings of the International Conference on Machine Learning and Applications, 2024

2023

Do all roads lead to Rome? Studying distance measures in the context of machine learning.

[BibT_eX]

[DOI]

Eva Blanco-Mallo

Beatriz Remeseiro

Juan Agustín Hitar-García

Pattern Recognit., September, 2023

Machine Learning Methods for Predicting League of Legends Game Outcome.

[BibT_eX]

[DOI]

Guillermo Castillo García

IEEE Trans. Games, June, 2023

Feature selection for domain adaptation using complexity measures and swarm intelligence.

[BibT_eX]

[DOI]

Neurocomputing, 2023

Logarithmic division for green feature selection: an information-theoretic approach.

[BibT_eX]

[DOI]

Guillermo Castillo García

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Efficient feature selection for domain adaptation using Mutual Information Maximization.

[BibT_eX]

[DOI]

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

Green Machine Learning.

[BibT_eX]

[DOI]

Brais Cancela

Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

2022

Low-precision feature selection on microarray data: an information theoretic approach.

[BibT_eX]

[DOI]

Medical Biol. Eng. Comput., 2022

How important is data quality? Best classifiers vs best features.

[BibT_eX]

[DOI]

Neurocomputing, 2022

Reduced precision discretization based on information theory.

[BibT_eX]

[DOI]

Brais Ares

Proceedings of the Knowledge-Based and Intelligent Information & Engineering Systems: Proceedings of the 26th International Conference KES-2022, 2022

Less is more: Low-precision feature selection for wearables.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2022

Feature selection for transfer learning using particle swarm optimization and complexity measures.

[BibT_eX]

[DOI]

Guillermo Castillo García

Proceedings of the 30th European Symposium on Artificial Neural Networks, 2022

Feature Selection: From the Past to the Future.

[BibT_eX]

[DOI]

Brais Cancela

Proceedings of the Advances in Selected Artificial Intelligence Areas, 2022

2021

Dimensionality Reduction: Is Feature Selection More Effective Than Random Selection?

[BibT_eX]

[DOI]

Proceedings of the Advances in Computational Intelligence, 2021

2020

Feature selection with limited bit depth mutual information for portable embedded systems.

[BibT_eX]

[DOI]

Konstantinos Sechidis

Gavin Brown

Knowl. Based Syst., 2020

When Size Matters: Markov Blanket with Limited Bit Depth Conditional Mutual Information.

[BibT_eX]

[DOI]

Eva Blanco-Mallo

Konstantinos Sechidis

Proceedings of the IoT Streams for Data-Driven Predictive Maintenance and IoT, Edge, and Mobile for Embedded Machine Learning, 2020

Do we need hundreds of classifiers or a good feature selection?

[BibT_eX]

[DOI]

Proceedings of the 28th European Symposium on Artificial Neural Networks, 2020

2019

Distributed classification based on distances between probability distributions in feature space.

[BibT_eX]

[DOI]

Pablo Montero-Manso

José Antonio Vilar

Inf. Sci., 2019

2017

On the use of different base classifiers in multiclass problems.

[BibT_eX]

[DOI]

Prog. Artif. Intell., 2017

Centralized vs. distributed feature selection methods based on data complexity measures.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2017

Can classification performance be predicted by complexity measures? A study using microarray data.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2017

A distributed approach for classification using distance metrics.

[BibT_eX]

[DOI]

Proceedings of the 25th European Symposium on Artificial Neural Networks, 2017

2016

Data complexity measures for analyzing the effect of SMOTE over microarrays.

[BibT_eX]

[DOI]

Proceedings of the 24th European Symposium on Artificial Neural Networks, 2016

Selection of the Best Base Classifier in One-Versus-One Using Data Complexity Measures.

[BibT_eX]

[DOI]

Proceedings of the Advances in Artificial Intelligence, 2016

2015

An insight on complexity measures and classification in microarray data.

[BibT_eX]

[DOI]

Proceedings of the 2015 International Joint Conference on Neural Networks, 2015

A Time Efficient Approach for Distributed Feature Selection Partitioning by Features.

[BibT_eX]

[DOI]