Juan José Rodríguez Diez

José Luis Garrido-Labrador

Ismael Ramos-Pérez

Samuel L. Hennessey

José Antonio Barbero-Aparicio

Inf. Fusion, April, 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

Inf. Fusion, February, 2024

2023

An experiment on animal re-identification from video.

[BibT_eX]

[DOI]

José Luis Garrido-Labrador

Ismael Ramos-Pérez

Samuel L. Hennessey

Ecol. Informatics, May, 2023

2022

Correction to: Rotation Forest for multi-target regression.

[BibT_eX]

[DOI]

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

Rotation Forest for multi-target regression.

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

Ismael Ramos-Pérez

María Consuelo Sáiz-Manzanares

Expert Syst. Appl., 2022

Combination of Object Tracking and Object Detection for Animal Recognition.

[BibT_eX]

[DOI]

Proceedings of the 5th IEEE International Conference on Image Processing Applications and Systems, 2022

A Benchmark Database for Animal Re-Identification and Tracking.

[BibT_eX]

[DOI]

Proceedings of the 5th IEEE International Conference on Image Processing Applications and Systems, 2022

2021

Improve teaching with modalities and collaborative groups in an LMS: an analysis of monitoring using visualisation techniques.

[BibT_eX]

[DOI]

Raúl Marticorena Sánchez

Sandra Rodríguez-Arribas

Yi Peng Ji

J. Comput. High. Educ., 2021

Rotation Forest for Big Data.

[BibT_eX]

[DOI]

Inf. Fusion, 2021

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

[BibT_eX]

[DOI]

Neurocomputing, 2021

Experimental evaluation of ensemble classifiers for imbalance in Big Data.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2021

2020

Random Balance ensembles for multiclass imbalance learning.

[BibT_eX]

[DOI]

Knowl. Based Syst., 2020

An experimental evaluation of mixup regression forests.

[BibT_eX]

[DOI]

Expert Syst. Appl., 2020

Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale.

[BibT_eX]

[DOI]

Clare E. Matthews

CoRR, 2020

2019

Combining univariate approaches for ensemble change detection in multivariate data.

[BibT_eX]

[DOI]

William J. Faithfull

Inf. Fusion, 2019

2018

On feature selection protocols for very low-sample-size data.

[BibT_eX]

[DOI]

Pattern Recognit., 2018

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

[BibT_eX]

[DOI]

Expert Syst. Appl., 2018

Local sets for multi-label instance selection.

[BibT_eX]

[DOI]

Appl. Soft Comput., 2018

2017

Restricted set classification: Who is there?

[BibT_eX]

[DOI]

Aaron S. Jackson

Pattern Recognit., 2017

A decision-making tool based on decision trees for roughness prediction in face milling.

[BibT_eX]

[DOI]

Guillem Quintana

Andrés Bustillo

Joaquim Ciurana

Int. J. Comput. Integr. Manuf., 2017

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

Instance selection for regression: Adapting DROP.

[BibT_eX]

[DOI]

Neurocomputing, 2016

Instance selection for regression by discretization.

[BibT_eX]

[DOI]

Carlos J. Alonso-González

Expert Syst. Appl., 2016

2015

Stacking for multivariate time series classification.

[BibT_eX]

[DOI]

Oscar J. Prieto

Pattern Anal. Appl., 2015

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

[BibT_eX]

[DOI]

Knowl. Based Syst., 2015

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

[BibT_eX]

[DOI]

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

A weighted voting framework for classifiers ensembles.

[BibT_eX]

[DOI]

Knowl. Inf. Syst., 2014

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

[BibT_eX]

[DOI]

Inf. Fusion, 2014

Online breakage detection of multitooth tools using classifier ensembles for imbalanced data.

[BibT_eX]

[DOI]

Andrés Bustillo

Int. J. Syst. Sci., 2014

2013

Interval feature extraction for classification of event-related potentials (ERP) in EEG data analysis.

[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]

Jesús Manuel Maudes Raedo

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

Improvements in Modelling of Complex Manufacturing Processes Using Classification Techniques.

[BibT_eX]

[DOI]

Proceedings of the Recent Trends in Applied Artificial Intelligence, 2013

2012

Supervised subspace projections for constructing ensembles of classifiers.

[BibT_eX]

[DOI]

Nicolás García-Pedrajas

Inf. Sci., 2012

Random feature weights for decision tree ensemble construction.

[BibT_eX]

[DOI]

Nicolás García-Pedrajas

Inf. Fusion, 2012

Classifier Ensemble Methods for Diagnosing COPD from Volatile Organic Compounds in Exhaled Air.

[BibT_eX]

[DOI]

Yasir Iftikhar Syed

Christopher O. Phillips

Keir Edward Lewis

Int. J. Knowl. Discov. Bioinform., 2012

Linear Projection Methods - An Experimental Study for Regression Problems.

[BibT_eX]

Carlos Pardo-Aguilar

Nicolás García-Pedrajas

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

Modelling of process parameters in laser polishing of steel components using ensembles of regression trees.

[BibT_eX]

[DOI]

Int. J. Comput. Integr. Manuf., 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 Ensembles of Regression Trees to Monitor Lubricating Oil Quality.

[BibT_eX]

[DOI]

Proceedings of the Modern Approaches in Applied Intelligence, 2011

Using Model Trees and Their Ensembles for Imbalanced Data.

[BibT_eX]

[DOI]

Pedro Santos

Proceedings of the Advances in Artificial Intelligence, 2011

2010

Random Subspace Ensembles for fMRI Classification.

[BibT_eX]

[DOI]

Catrin O. Plumpton

David E. J. Linden

Stephen J. Johnston

IEEE Trans. Medical Imaging, 2010

Forests of nested dichotomies.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2010

Finding optimal classifiers for small feature sets in genomics and proteomics.

[BibT_eX]

[DOI]

Gregor Stiglic

Peter Kokol

Neurocomputing, 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]

Carlos J. Alonso-González

Proceedings of the Trends in Applied Intelligent Systems, 2010

Ensemble Methods and Model Based Diagnosis Using Possible Conflicts and System Decomposition.

[BibT_eX]

[DOI]

Oscar J. Prieto

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

Boosting recombined weak classifiers.

[BibT_eX]

[DOI]

Pattern Recognit. Lett., 2008

Combining Online Classification Approaches for Changing Environments.

[BibT_eX]

[DOI]

Proceedings of the Structural, 2008

Feature Selection and Classification for Small Gene Sets.

[BibT_eX]

[DOI]

Gregor Stiglic

Peter Kokol

Proceedings of the Pattern Recognition in Bioinformatics, 2008

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

[BibT_eX]

Proceedings of the ICINCO 2008, 2008

2007

Classifier Ensembles with a Random Linear Oracle.

[BibT_eX]

[DOI]

IEEE Trans. Knowl. Data Eng., 2007

Diagnosing scrapie in sheep: A classification experiment.

[BibT_eX]

[DOI]

Victor J. del Rio Vilas

Comput. Biol. Medicine, 2007

Naïve Bayes Ensembles with a Random Oracle.

[BibT_eX]

[DOI]

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

Cascading for Nominal Data.

[BibT_eX]

[DOI]

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

An Experimental Study on Rotation Forest Ensembles.

[BibT_eX]

[DOI]

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

Rotation Forest and Random Oracles: Two Classifier Ensemble Methods.

[BibT_eX]

[DOI]

Proceedings of the 20th IEEE International Symposium on Computer-Based Medical Systems (CBMS 2007), 2007

Stacking Dynamic Time Warping for the Diagnosis of Dynamic Systems.

[BibT_eX]

[DOI]

Proceedings of the Current Topics in Artificial Intelligence, 2007

2006

Rotation Forest: A New Classifier Ensemble Method.

[BibT_eX]

[DOI]

IEEE Trans. Pattern Anal. Mach. Intell., 2006

Rotation-based ensembles of RBF networks.

[BibT_eX]

[DOI]

Proceedings of the 14th European Symposium on Artificial Neural Networks, 2006

Ensembles of Grafted Trees.

[BibT_eX]

Proceedings of the ECAI 2006, 17th European Conference on Artificial Intelligence, August 29, 2006

2005

Support vector machines of interval-based features for time series classification.

[BibT_eX]

[DOI]

José A. Maestro

Knowl. Based Syst., 2005

Bias and Variance of Rotation-Based Ensembles.

[BibT_eX]

[DOI]

María Aránzazu Simón Hurtado

Oscar J. Prieto

Proceedings of the Computational Intelligence and Bioinspired Systems, 2005

Early Fault Classification in Dynamic Systems Using Case-Based Reasoning.

[BibT_eX]

[DOI]

Aníbal Bregón

Q. Isaac Moro

Proceedings of the Current Topics in Artificial Intelligence, 2005

2004

Clasificación de Series: Máquinas de Vectores Soporte y Literales basados en Intervalos.

[BibT_eX]

[DOI]

Inteligencia Artif., 2004

Support Vector Machines of Interval-based Features for Time Series Classification.

[BibT_eX]

[DOI]

Proceedings of the Research and Development in Intelligent Systems XXI, 2004

Interval and dynamic time warping-based decision trees.

[BibT_eX]

[DOI]

Proceedings of the 2004 ACM Symposium on Applied Computing (SAC), 2004

2003

RBF Networks from Boosted Rules.

[BibT_eX]

Proceedings of the ACIS Fourth International Conference on Software Engineering, 2003

Rotation-Based Ensembles.

[BibT_eX]

[DOI]

Proceedings of the Current Topics in Artificial Intelligence, 2003

Enhancing Consistency Based Diagnosis with Machine Learning Techniques.

[BibT_eX]

[DOI]

Proceedings of the Current Topics in Artificial Intelligence, 2003

2001

Learning microcontrollers with a CAI oriented multi-micro simulation environment.

[BibT_eX]

[DOI]

Alfredo del Río

Andrés A. Nogueiras Meléndez

IEEE Trans. Educ., 2001

Boosting interval based literals.

[BibT_eX]

[DOI]

Henrik Boström

Intell. Data Anal., 2001

Clasificación de Patrones Temporales en Sistemas Dinámicos mediante Boosting y Alineamiento Dinámico Temporal.

[BibT_eX]

[DOI]

Q. Isaac Moro-Sancho

Computación y Sistemas, 2001

Learning Classification RBF Networks by Boosting.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems, Second International Workshop, 2001

2000

Time Series Classification by Boosting Interval Based Literals.

[BibT_eX]

[DOI]

Inteligencia Artif., 2000

Learning First Order Logic Time Series Classifiers: Rules and Boosting.

[BibT_eX]

[DOI]

Henrik Boström

Proceedings of the Principles of Data Mining and Knowledge Discovery, 2000

Applying Boosting to Similarity Literals for Time Series Classification.

[BibT_eX]

[DOI]

Proceedings of the Multiple Classifier Systems, First International Workshop, 2000

Learning First Order Logic Time Series Classifiers.

[BibT_eX]

[DOI]

Henrik Boström

Proceedings of the Inductive Logic Programming, 10th International Conference, 2000

1999

Obtaining Generic Classes Automatically through a Parameterization Operator: A Focus on Constrained Genericity.

[BibT_eX]

[DOI]

Yania Crespo

José Manuel Marqués Corral

Proceedings of the TOOLS 1999: 31st International Conference on Technology of Object-Oriented Languages and Systems, 1999

Obtención Automática de Clases Genéricas a Través de una Operación de Parametrización.

[BibT_eX]

Yania Crespo