Self-Organizing Cases to Find Paradigms. - Juan José del Coz; Oscar Luaces; José Ramón Quevedo; Jaime Alonso 001; José Ranilla; Antonio Bahamonde (IWANN (1), 1999)

Autonomous Clustering for Machine Learning. - Oscar Luaces; Juan José del Coz; José Ramón Quevedo; Jaime Alonso 001; José Ranilla; Antonio Bahamonde (IWANN (1), 1999)

Un sistema inteligente para calificar morfológicamente a bovinos de la raza Asturiana de los Valles. - F. López; Félix Goyache; José Ramón Quevedo; Jaime Alonso 001; José Ranilla; Oscar Luaces; Antonio Bahamonde; Juan José del Coz (Inteligencia Artif., 2000)

Forecasting Time Series Combining Machine Learning and Box-Jenkins Time Series. - Elena Montañés; José Ramón Quevedo; Maria M. Prieto; César O. Menéndez (IBERAMIA, 2002)

Filtering Noisy Continuous Labeled Examples. - José Ramón Quevedo Pérez; María Dolores García; Elena Montañés (IBERAMIA, 2002)

A Wrapper Approach with Support Vector Machines for Text Categorization. - Elena Montañés; José Ramón Quevedo; Irene Díaz (IWANN (1), 2003)

Choosing among Algorithms to Improve Accuracy. - José Ramón Quevedo; Elías F. Combarro; Antonio Bahamonde (IWANN (1), 2003)

Discovering Relevancies in Very Difficult Regression Problems: Applications to Sensory Data Analysis. - Jorge Díez 001; Gustavo F. Bayón; José Ramón Quevedo; Juan José del Coz; Oscar Luaces; Jaime Alonso 001; Antonio Bahamonde (ECAI, 2004)

Analyzing Sensory Data Using Non-linear Preference Learning with Feature Subset Selection. - Oscar Luaces; Gustavo F. Bayón; José Ramón Quevedo; Jorge Díez 001; Juan José del Coz; Antonio Bahamonde (ECML, 2004)

Words as Rules: Feature Selection in Text Categorization. - Elena Montañés; Elías F. Combarro; Irene Díaz; José Ranilla; José Ramón Quevedo (International Conference on Computational Science, 2004)

Feature subset selection for learning preferences: a case study. - Antonio Bahamonde; Gustavo F. Bayón; Jorge Díez 001; José Ramón Quevedo; Oscar Luaces; Juan José del Coz; Jaime Alonso 001; Félix Goyache (ICML, 2004)

Prediction of Probability of Survival in Critically Ill Patients Optimizing the Area under the ROC Curve. - Oscar Luaces; José Ramón Quevedo; Francisco Taboada; Guillermo M. Albaiceta; Antonio Bahamonde (IJCAI, 2007)

A simple and efficient method for variable ranking according to their usefulness for learning. - José Ramón Quevedo; Antonio Bahamonde; Oscar Luaces (Comput. Stat. Data Anal., 2007)

A Hybrid Feature Selection Method for Text Categorization. - Elena Montañés; José Ramón Quevedo; Elías F. Combarro; Irene Díaz; José Ranilla (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2007)

Obtaining weights of a rubric through a pairwise learning model when the assessment process involves more than one lecturer. - José Ramón Quevedo; Elena Montañés (EDM, 2009)

Prediction and Inheritance of Phenotypes. - Antonio Bahamonde; Jaime Alonso 001; Juan José del Coz; Jorge Díez 001; José Ramón Quevedo; Oscar Luaces (IWINAC (1), 2009)

Collaborative Tag Recommendation System based on Logistic Regression. - Elena Montañés; José Ramón Quevedo; Irene Díaz; José Ranilla (DC@PKDD/ECML, 2009)

Ranked Tag Recommendation Systems Based on Logistic Regression. - José Ramón Quevedo; Elena Montañés; José Ranilla; Irene Díaz (HAIS (1), 2010)

Viability of an Alarm Predictor for Coffee Rust Disease Using Interval Regression. - Oscar Luaces; Luiz Henrique A. Rodrigues; Carlos Alberto Alves Meira; José Ramón Quevedo; Antonio Bahamonde (IEA/AIE (2), 2010)

Adapting Decision DAGs for Multipartite Ranking. - José Ramón Quevedo; Elena Montañés; Oscar Luaces; Juan José del Coz (ECML/PKDD (3), 2010)

Explaining the Genetic Basis of Complex Quantitative Traits through Prediction Models. - Oscar Luaces; José Ramón Quevedo; Miguel Pérez-Enciso; Jorge Díez 001; Juan José del Coz; Antonio Bahamonde (J. Comput. Biol., 2010)

Graphical Feature Selection for Multilabel Classification Tasks. - Gerardo Lastra; Oscar Luaces; José Ramón Quevedo; Antonio Bahamonde (IDA, 2011)

Aggregating Independent and Dependent Models to Learn Multi-label Classifiers. - Elena Montañés; José Ramón Quevedo; Juan José del Coz (ECML/PKDD (2), 2011)

A greedy algorithm for dimensionality reduction in polynomial regression to forecast the performance of a power plant condenser. - Elena Montañés; José Ramón Quevedo; Maria M. Prieto (Intell. Data Anal., 2011)

TagRanker: learning to recommend ranked tags. - Elena Montañés; José Ramón Quevedo; Irene Díaz; Raquel Cortina; Pedro Alonso 001; José Ranilla (Log. J. IGPL, 2011)

Multilabel classifiers with a probabilistic thresholding strategy. - José Ramón Quevedo; Oscar Luaces; Antonio Bahamonde (Pattern Recognit., 2012)

Disease Liability Prediction from Large Scale Genotyping Data Using Classifiers with a Reject Option. - José Ramón Quevedo; Antonio Bahamonde; Miguel Pérez-Enciso; Oscar Luaces (IEEE ACM Trans. Comput. Biol. Bioinform., 2012)

Feature selection for classification of animal feed ingredients from near infrared microscopy spectra. - José Antonio Sánchez del Rivero; Elena Montañés-Roces; Begoña de la Roza-Delgado; Ana Soldado; Oscar Luaces; José Ramón Quevedo; Antonio Bahamonde (Inf. Sci., 2013)

Ordinal classification/regression for analyzing the influence of superstars on spectators in cinema marketing. - Elena Montañés; Ana Suárez-Vázquez; José Ramón Quevedo (Expert Syst. Appl., 2014)

Dependent binary relevance models for multi-label classification. - Elena Montañés; Robin Senge; José Barranquero; José Ramón Quevedo; Juan José del Coz; Eyke Hüllermeier (Pattern Recognit., 2014)

Using A* for Inference in Probabilistic Classifier Chains. - Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (IJCAI, 2015)

An overview of inference methods in probabilistic classifier chains for multilabel classification. - Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (WIREs Data Mining Knowl. Discov., 2016)

Using ensembles for problems with characterizable changes in data distribution: A case study on quantification. - Pablo Pérez-Gállego; José Ramón Quevedo; Juan José del Coz (Inf. Fusion, 2017)

A heuristic in A* for inference in nonlinear Probabilistic Classifier Chains. - Deiner Mena; José Ramón Quevedo; Elena Montañés; Juan José del Coz (Knowl. Based Syst., 2017)

A family of admissible heuristics for A* to perform inference in probabilistic classifier chains. - Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (Mach. Learn., 2017)

Dynamic ensemble selection for quantification tasks. - Pablo Pérez-Gállego; Alberto Castaño; José Ramón Quevedo; Juan José del Coz (Inf. Fusion, 2019)

Improving the ε-approximate algorithm for Probabilistic Classifier Chains. - Miriam Fdez-Díaz; Laura Fdez-Díaz; Deiner Mena; Elena Montañés; José Ramón Quevedo; Juan José del Coz (Knowl. Inf. Syst., 2020)

Capturing waste collection planning expert knowledge in a fitness function through preference learning. - Laura Fdez-Díaz; Miriam Fdez-Díaz; José Ramón Quevedo; Elena Montañés (Eng. Appl. Artif. Intell., 2021)

Improving importance estimation in covariate shift for providing accurate prediction error. - Laura Fdez-Díaz; Sara Glez-Tomillo; Elena Montañés; José Ramón Quevedo (Expert Syst. Appl., 2022)

Target inductive methods for zero-shot regression. - Miriam Fdez-Díaz; José Ramón Quevedo; Elena Montañés (Inf. Sci., 2022)

Regularized boosting with an increasing coefficient magnitude stop criterion as meta-learner in hyperparameter optimization stacking ensemble. - Laura Fdez-Díaz; José Ramón Quevedo; Elena Montañés (Neurocomputing, 2023)

Direct side information learning for zero-shot regression. - Miriam Fdez-Díaz; Elena Montañés; José Ramón Quevedo (Neurocomputing, 2023)