César Ignacio García-Osorio

José Antonio Barbero-Aparicio

SoftwareX, 2025

2024

Addressing data scarcity in protein fitness landscape analysis: A study on semi-supervised and deep transfer learning techniques.

[BibT_eX]

[DOI]

Alicia Olivares-Gil

José Luis Garrido-Labrador

Inf. Fusion, February, 2024

Ensemble methods and semi-supervised learning for information fusion: A review and future research directions.

[BibT_eX]

[DOI]

Ana Serrano Mamolar

Jesús Manuel Maudes Raedo

Inf. Fusion, 2024

WALGREEN: Web Based Platform for Soil Organic Carbon Inference Applications.

[BibT_eX]

[DOI]

José Manuel Aroca

José Antonio Barbero-Aparicio

Pedro Latorre-Carmona

Proceedings of the IGARSS 2024, 2024

2023

Deep learning and support vector machines for transcription start site identification.

[BibT_eX]

[DOI]

Alicia Olivares-Gil

José Antonio Barbero-Aparicio

PeerJ Comput. Sci., 2023

2022

Nonlinear physics opens a new paradigm for accurate transcription start site prediction.

[BibT_eX]

[DOI]

Santiago Cuesta-López

Javier Pérez-Rodríguez

BMC Bioinform., December, 2022

When is resampling beneficial for feature selection with imbalanced wide data?

[BibT_eX]

[DOI]

Ismael Ramos-Pérez

Expert Syst. Appl., 2022

Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework.

[BibT_eX]

[DOI]

Francisco Javier Yagüe

Pedro Latorre-Carmona

CoRR, 2022

Proposal of a Comparative Framework for Face Super-Resolution Algorithms in Forensics.

[BibT_eX]

[DOI]

Antonio Salguero-Cruz

Pedro Latorre-Carmona

Proceedings of the Pattern Recognition and Image Analysis - 10th Iberian Conference, 2022

2021

Rotation Forest for Big Data.

[BibT_eX]

[DOI]

Mario Juez-Gil

Carlos López Nozal

Inf. Fusion, 2021

Approx-SMOTE: Fast SMOTE for Big Data on Apache Spark.

[BibT_eX]

[DOI]

Mario Juez-Gil

Carlos López Nozal

Neurocomputing, 2021

Experimental evaluation of ensemble classifiers for imbalance in Big Data.

[BibT_eX]

[DOI]

Mario Juez-Gil

Appl. Soft Comput., 2021

2019

Evolutionary prototype selection for multi-output regression.

[BibT_eX]

[DOI]

Miroslaw Kordos

Neurocomputing, 2019

2018

Study of data transformation techniques for adapting single-label prototype selection algorithms to multi-label learning.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2018

Seshat - a web-based educational resource for teaching the most common algorithms of lexical analysis.

[BibT_eX]

[DOI]

Ismael Ramos-Pérez

Comput. Appl. Eng. Educ., 2018

Local sets for multi-label instance selection.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2018

2016

Random feature weights for regression trees.

[BibT_eX]

[DOI]

Prog. Artif. Intell., 2016

Instance selection of linear complexity for big data.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2016

Fusion of instance selection methods in regression tasks.

[BibT_eX]

[DOI]

Marcin Blachnik

Miroslaw Kordos

Inf. Fusion, 2016

Instance selection for regression: Adapting DROP.

[BibT_eX]

[DOI]

Neurocomputing, 2016

Instance selection for regression by discretization.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2016

2015

Random Balance: Ensembles of variable priors classifiers for imbalanced data.

[BibT_eX]

[DOI]

Ludmila I. Kuncheva

Knowl. Based Syst., 2015

Diversity techniques improve the performance of the best imbalance learning ensembles.

[BibT_eX]

[DOI]

Ludmila I. Kuncheva

Inf. Sci., 2015

An Experimental Study on Combining Binarization Techniques and Ensemble Methods of Decision Trees.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems - 12th International Workshop, 2015

2014

Tree ensemble construction using a GRASP-based heuristic and annealed randomness.

[BibT_eX]

[DOI]

Inf. Fusion, 2014

2013

Boosting for class-imbalanced datasets using genetically evolved supervised non-linear projections.

[BibT_eX]

[DOI]

Prog. Artif. Intell., 2013

Rotation Forests for regression.

[BibT_eX]

[DOI]

Appl. Math. Comput., 2013

Random Oracle Ensembles for Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems, 11th International Workshop, 2013

Imbalanced Learning Ensembles for Defect Detection in X-Ray Images.

[BibT_eX]

[DOI]

Víctor Barbero-García

Alan Blanco-Álamo

Proceedings of the Recent Trends in Applied Artificial Intelligence, 2013

2012

Boosting Projections to improve surface roughness prediction in high-torque milling operations.

[BibT_eX]

[DOI]

Andrés Bustillo

Guillem Quintana

Soft Comput., 2012

Supervised subspace projections for constructing ensembles of classifiers.

[BibT_eX]

[DOI]

Jesús Manuel Maudes Raedo

Inf. Sci., 2012

Random feature weights for decision tree ensemble construction.

[BibT_eX]

[DOI]

Inf. Fusion, 2012

Linear Projection Methods - An Experimental Study for Regression Problems.

[BibT_eX]

Carlos Pardo-Aguilar

Proceedings of the ICPRAM 2012, 2012

Disturbing Neighbors Ensembles of Trees for Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the 11th International Conference on Machine Learning and Applications, 2012

2011

Random Oracles for Regression Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Ensembles in Machine Learning Applications, 2011

Constructing ensembles of classifiers using supervised projection methods based on misclassified instances.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2011

Random projections for linear SVM ensembles.

[BibT_eX]

[DOI]

Appl. Intell., 2011

Ensembles of Decision Trees for Imbalanced Data.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems - 10th International Workshop, 2011

GRASP Forest: A New Ensemble Method for Trees.

[BibT_eX]

[DOI]

Andrés Bustillo

Proceedings of the Multiple Classifier Systems - 10th International Workshop, 2011

Using Model Trees and Their Ensembles for Imbalanced Data.

[BibT_eX]

[DOI]

Pedro Santos

Proceedings of the Advances in Artificial Intelligence, 2011

2010

Forests of nested dichotomies.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2010

Democratic instance selection: A linear complexity instance selection algorithm based on classifier ensemble concepts.

[BibT_eX]

[DOI]

Aida de Haro-García

Artif. Intell., 2010

An Experimental Study on Ensembles of Functional Trees.

[BibT_eX]

[DOI]

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

An Empirical Study of Multilayer Perceptron Ensembles for Regression Tasks.

[BibT_eX]

[DOI]

Proceedings of the Trends in Applied Intelligent Systems, 2010

Random Projections for SVM Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Trends in Applied Intelligent Systems, 2010

2009

Disturbing Neighbors Diversity for Decision Forests.

[BibT_eX]

[DOI]

Proceedings of the Applications of Supervised and Unsupervised Ensemble Methods, 2009

Disturbing Neighbors Ensembles for Linear SVM.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems, 8th International Workshop, 2009

2008

Teaching push-down automata and turing machines.

[BibT_eX]

[DOI]

Iñigo Mediavilla-Sáiz

Javier Jimeno-Visitación

Proceedings of the 13th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, 2008

A tool for teaching LL and LR parsing algorithms.

[BibT_eX]

[DOI]

Carlos Gómez-Palacios

Proceedings of the 13th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, 2008

License Plate Number Recognition - New Heuristics and a Comparative Study of Classifiers.

[BibT_eX]

Proceedings of the ICINCO 2008, 2008

Constructing ensembles of classifiers using linear projections based on misclassified instances.

[BibT_eX]

[DOI]

Proceedings of the 16th European Symposium on Artificial Neural Networks, 2008

2007

Nonlinear Boosting Projections for Ensemble Construction.

[BibT_eX]

[DOI]

J. Mach. Learn. Res., 2007

Cascading for Nominal Data.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems, 7th International Workshop, 2007

2005

Data mining and visualization.

[BibT_eX]

[DOI]

PhD thesis, 2005

Visualization of High-dimensional Data via Orthogonal Curves.

[BibT_eX]

[DOI]

J. Univers. Comput. Sci., 2005

Regaining sparsity in kernel principal components.

[BibT_eX]

[DOI]

Neurocomputing, 2005

The combined use of self-organizing maps and andrews' curves.

[BibT_eX]

[DOI]

Int. J. Neural Syst., 2005

2004

Using Andrews Curves for Clustering and Sub-clustering Self-Organizing Maps.

[BibT_eX]

[DOI]