Gonzalo Martínez-Muñoz

Miguel Ángel Álvarez-Rodríguez

Estrella Pulido-Cañabate

IEEE Trans. Educ., August, 2024

Feature importance analysis for a highly unbalanced multiple myeloma data classification.

[BibT_eX]

[DOI]

Rima Guilal

Nesma Settouti

Mohammed Amine Chikh

Int. J. Medical Eng. Informatics, 2024

Deep Learning for Multi-Output Regression Using Gradient Boosting.

[BibT_eX]

[DOI]

IEEE Access, 2024

2023

A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks.

[BibT_eX]

[DOI]

CoRR, 2023

Sequential Training of Neural Networks With Gradient Boosting.

[BibT_eX]

[DOI]

IEEE Access, 2023

Multi-Task Gradient Boosting.

[BibT_eX]

[DOI]

Carlos Ruiz Pastor

Proceedings of the Hybrid Artificial Intelligent Systems - 18th International Conference, 2023

2022

An analysis of heuristic metrics for classifier ensemble pruning based on ordered aggregation.

[BibT_eX]

[DOI]

Amgad M. Mohammed

Enrique Onieva

Michal Wozniak

Pattern Recognit., 2022

Correction to: Building heterogeneous ensembles by pooling homogeneous ensembles.

[BibT_eX]

[DOI]

Int. J. Mach. Learn. Cybern., 2022

Building heterogeneous ensembles by pooling homogeneous ensembles.

[BibT_eX]

[DOI]

Int. J. Mach. Learn. Cybern., 2022

Condensed Gradient Boosting.

[BibT_eX]

[DOI]

CoRR, 2022

SVM Ensembles on a Budget.

[BibT_eX]

[DOI]

David Nevado

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

Multioutput Regression Neural Network Training via Gradient Boosting.

[BibT_eX]

[DOI]

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

2021

A comparative analysis of gradient boosting algorithms.

[BibT_eX]

[DOI]

Candice Bentéjac

Anna Csörgo

Vicenzo Abichequer Sangalli

Artif. Intell. Rev., 2021

2020

Identifying Cheating Users in Online Courses.

[BibT_eX]

[DOI]

Estrella Pulido-Cañabate

Proceedings of the 2020 IEEE Global Engineering Education Conference, 2020

2019

A Comparative Analysis of XGBoost.

[BibT_eX]

[DOI]

Candice Bentéjac

Anna Csörgo

CoRR, 2019

Sequential Training of Neural Networks with Gradient Boosting.

[BibT_eX]

[DOI]

CoRR, 2019

2018

Vote-boosting ensembles.

[BibT_eX]

[DOI]

Pattern Recognit., 2018

A two-stage ensemble method for the detection of class-label noise.

[BibT_eX]

[DOI]

Neurocomputing, 2018

Pooling homogeneous ensembles to build heterogeneous ensembles.

[BibT_eX]

[DOI]

CoRR, 2018

Using Bag-of-Little Bootstraps for Efficient Ensemble Learning.

[BibT_eX]

[DOI]

Pablo de Viña

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

Randomization vs Optimization in SVM Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2018, 2018

2017

Analysing Event Transitions to Discover Student Roles and Predict Grades in MOOCs.

[BibT_eX]

[DOI]

Ángel Pérez-Lemonche

Estrella Pulido-Cañabate

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2017, 2017

2016

An urn model for majority voting in classification ensembles.

[BibT_eX]

[DOI]

Víctor Soto

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Small margin ensembles can be robust to class-label noise.

[BibT_eX]

[DOI]

Neurocomputing, 2015

Special Issue on "Solving complex machine learning problems with ensemble methods".

[BibT_eX]

[DOI]

Ioannis Katakis

Ioannis Partalas

Neurocomputing, 2015

Using a SPOC to flip the classroom.

[BibT_eX]

[DOI]

Estrella Pulido

Proceedings of the IEEE Global Engineering Education Conference, 2015

2014

A Double Pruning Scheme for Boosting Ensembles.

[BibT_eX]

[DOI]

Víctor Soto

Sergio García-Moratilla

Luis Fernando Lago-Fernández

IEEE Trans. Cybern., 2014

Cluster validation in problems with increasing dimensionality and unbalanced clusters.

[BibT_eX]

[DOI]

Jesús Aragón

Manuel A. Sánchez-Montañés

Ana M. González

Neurocomputing, 2014

Improving the Robustness of Bagging with Reduced Sampling Size.

[BibT_eX]

[DOI]

Proceedings of the 22th European Symposium on Artificial Neural Networks, 2014

2013

How large should ensembles of classifiers be?

[BibT_eX]

[DOI]

Luis Fernando Lago-Fernández

Pattern Recognit., 2013

2012

Evaluation of Negentropy-based Cluster Validation Techniques in Problems with Increasing Dimensionality.

[BibT_eX]

Manuel A. Sánchez-Montañés

Ana M. González

Proceedings of the ICPRAM 2012, 2012

On the Independence of the Individual Predictions in Parallel Randomized Ensembles.

[BibT_eX]

[DOI]

Proceedings of the 20th European Symposium on Artificial Neural Networks, 2012

2011

Inference on the prediction of ensembles of infinite size.

[BibT_eX]

[DOI]

Pattern Recognit., 2011

Empirical analysis and evaluation of approximate techniques for pruning regression bagging ensembles.

[BibT_eX]

[DOI]

Luis Fernando Lago-Fernández

Neurocomputing, 2011

A comparison of techniques for robust gender recognition.

[BibT_eX]

[DOI]

Richard N. Rojas-Bello

Manuel A. Sánchez-Montañés

Proceedings of the 18th IEEE International Conference on Image Processing, 2011

2010

Out-of-bag estimation of the optimal sample size in bagging.

[BibT_eX]

[DOI]

Pattern Recognit., 2010

A Double Pruning Algorithm for Classification Ensembles.

[BibT_eX]

[DOI]

Víctor Soto

Proceedings of the Multiple Classifier Systems, 9th International Workshop, 2010

Haar Random Forest Features and SVM Spatial Matching Kernel for Stonefly Species Identification.

[BibT_eX]

[DOI]

Natalia Larios

Bilge Soran

Linda G. Shapiro

Junyuan Lin

Thomas G. Dietterich

Proceedings of the 20th International Conference on Pattern Recognition, 2010

2009

An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Statistical Instance-Based Pruning in Ensembles of Independent Classifiers.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2009

Statistical Instance-Based Ensemble Pruning for Multi-class Problems.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2009

Dictionary-free categorization of very similar objects via stacked evidence trees.

[BibT_eX]

[DOI]

Natalia Larios Delgado

Proceedings of the 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2009), 2009

2008

Class-switching neural network ensembles.

[BibT_eX]

[DOI]

Aitor Sánchez-Martínez

Neurocomputing, 2008

2007

Using boosting to prune bagging ensembles.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2007

Out of Bootstrap Estimation of Generalization Error Curves in Bagging Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Intelligent Data Engineering and Automated Learning, 2007

Selection of Decision Stumps in Bagging Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Artificial Neural Networks, 2007

2006

Pruning in Ordered Regression Bagging Ensembles.

[BibT_eX]

[DOI]

Proceedings of the International Joint Conference on Neural Networks, 2006

Evaluation of Decision Tree Pruning with Subadditive Penalties.

[BibT_eX]

[DOI]

Sergio García-Moratilla

Proceedings of the Intelligent Data Engineering and Automated Learning, 2006

Pruning in ordered bagging ensembles.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning, 2006

Building Ensembles of Neural Networks with Class-Switching.

[BibT_eX]

[DOI]

Aitor Sánchez-Martínez

Proceedings of the Artificial Neural Networks, 2006

2005

Switching class labels to generate classification ensembles.

[BibT_eX]

[DOI]

Pattern Recognit., 2005

2004

Using all data to generate decision tree ensembles.

[BibT_eX]

[DOI]