Genetic Algorithms Elitist Probabilistic of Degree 1, a generalization of Simulated Annealing. - Pedro Larrañaga; Manuel Graña; Alicia D'Anjou; Francisco Javier Torrealdea (AI*IA, 1993)

Structure learning approaches in Causal Probalistics Networks. - Pedro Larrañaga; Yosu Yurramendi (ECSQARU, 1993)

Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms. - Pedro Larrañaga; Roberto H. Murga; Mikel Poza; Cindy M. H. Kuijpers (AISTATS, 1995)

Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters. - Pedro Larrañaga; Mikel Poza; Yosu Yurramendi; Roberto H. Murga; Cindy M. H. Kuijpers (IEEE Trans. Pattern Anal. Mach. Intell., 1996)

Learning Bayesian network structures by searching for the best ordering with genetic algorithms. - Pedro Larrañaga; Cindy M. H. Kuijpers; Roberto H. Murga; Yosu Yurramendi (IEEE Trans. Syst. Man Cybern. Part A, 1996)

Learning Bayesisan Networks by Genetic Algorithms: A Case Study in the Prediction of Survival in Malignant Skin Melanoma. - Pedro Larrañaga; Basilio Sierra; Miren J. Gallego; Maria J. Michelena; Juan M. Picaza (AIME, 1997)

Bayesian Networks, Rule Induction and Logistic Regression in the Prediction of the Survival of Women Suffering from Breast Cancer. - Pedro Larrañaga; Miren J. Gallego; Basilio Sierra; L. Urkola; Maria J. Michelena (EPIA, 1997)

Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering. - Ana Isabel González; Manuel Graña; José Antonio Lozano 001; Pedro Larrañaga (ICANNGA, 1997)

Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data. - Ramon Etxeberria; Pedro Larrañaga; Juan M. Picaza (Pattern Recognit. Lett., 1997)

Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms. - Pedro Larrañaga; Cindy M. H. Kuijpers; Mikel Poza; Roberto H. Murga (Stat. Comput., 1997)

Structure of the high-order Boltzmann machine from independence maps. - F. Xabier Albizuri; Alicia D'Anjou; Manuel Graña; Pedro Larrañaga (IEEE Trans. Neural Networks, 1997)

Aplicación de los algoritmos genéticos al problema del clustering jerárquico. - José Antonio Lozano 001; Pedro Larrañaga (Inteligencia Artif., 1998)

Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches. - Basilio Sierra; Pedro Larrañaga (Artif. Intell. Medicine, 1998)

Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit. - Basilio Sierra; Nicolás Serrano; Pedro Larrañaga; Eliseo J. Plasencia; Iñaki Inza; Juan José Jiménez; Jose María De la Rosa; María Luisa Mora (AIMDM, 1999)

Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. - Pedro Larrañaga; Cindy M. H. Kuijpers; Roberto H. Murga; Iñaki Inza; S. Dizdarevic (Artif. Intell. Rev., 1999)

Representing the behaviour of supervised classification learning algorithms by Bayesian networks. - Iñaki Inza; Pedro Larrañaga; Basilio Sierra; Ramon Etxeberria; José Antonio Lozano 001; José M. Peña 001 (Pattern Recognit. Lett., 1999)

Applying genetic algorithms to search for the best hierarchical clustering of a dataset. - José Antonio Lozano 001; Pedro Larrañaga (Pattern Recognit. Lett., 1999)

An empirical comparison of four initialization methods for the K-Means algorithm. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Pattern Recognit. Lett., 1999)

Learning Bayesian networks for clustering by means of constructive induction. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Pattern Recognit. Lett., 1999)

Genetic Algorithms: Bridging the Convergence Gap. - José Antonio Lozano 001; Pedro Larrañaga; Manuel Graña; F. Xabier Albizuri (Theor. Comput. Sci., 1999)

Feature Subset Selection Using Probabilistic Tree Structures. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS. - Iñaki Inza; Marisa Merino; Pedro Larrañaga; Jorge Quiroga; Basilio Sierra; Marcos Girala (ISMDA, 2000)

Medical Bayes Networks. - Basilio Sierra; Iñaki Inza; Pedro Larrañaga (ISMDA, 2000)

Combinatonal Optimization by Learning and Simulation of Bayesian Networks. - Pedro Larrañaga; Ramon Etxeberria; José Antonio Lozano 001; José M. Peña 001 (UAI, 2000)

Feature Subset Selection by Bayesian network-based optimization. - Iñaki Inza; Pedro Larrañaga; Ramon Etxeberria; Basilio Sierra (Artif. Intell., 2000)

Analyzing the Population Based Incremental Learning Algorithm by Means of Discrete Dynamical Systems. - Cristina González; José Antonio Lozano 001; Pedro Larrañaga (Complex Syst., 2000)

An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Pattern Recognit. Lett., 2000)

Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS. - Basilio Sierra; Elena Lazkano; Iñaki Inza; Marisa Merino; Pedro Larrañaga; Jorge Quiroga (AIME, 2001)

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks. - José M. Peña 001; I. Izarzugaza; José Antonio Lozano 001; E. Aldasoro; Pedro Larrañaga (AISTATS, 2001)

Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems. - Endika Bengoetxea; Pedro Larrañaga; Isabelle Bloch; Aymeric Perchant (EMMCVPR, 2001)

On Applying Supervised Classification Techniques in Medicine. - Basilio Sierra; Iñaki Inza; Pedro Larrañaga (ISMDA, 2001)

Feature subset selection by genetic algorithms and estimation of distribution algorithms - A case study in the survival of cirrhotic patients treated with TIPS. - Iñaki Inza; Marisa Merino; Pedro Larrañaga; Jorge Quiroga; Basilio Sierra; Marcos Girala (Artif. Intell. Medicine, 2001)

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data. - Basilio Sierra; Nicolás Serrano; Pedro Larrañaga; Eliseo J. Plasencia; Iñaki Inza; Juan José Jiménez; Pedro Revuelta; María Luisa Mora (Artif. Intell. Medicine, 2001)

Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms. - Iñaki Inza; Pedro Larrañaga; Basilio Sierra (Int. J. Approx. Reason., 2001)

Performance evaluation of compromise conditional Gaussian networks for data clustering. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Int. J. Approx. Reason., 2001)

Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga; Iñaki Inza (IEEE Trans. Pattern Anal. Mach. Intell., 2001)

Solving Graph Matching with EDAs Using a Permutation-Based Representation. - Endika Bengoetxea; Pedro Larrañaga; Aymeric Perchant; Isabelle Bloch (Estimation of Distribution Algorithms, 2002)

Experimental Results in Function Optimization with EDAs in Continuous Domain. - Endika Bengoetxea; Teresa Miquélez; Pedro Larrañaga; José Antonio Lozano 001 (Estimation of Distribution Algorithms, 2002)

Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs. - L. M. Campos; José A. Gámez 001; Pedro Larrañaga; Serafín Moral; Txomin Romero (Estimation of Distribution Algorithms, 2002)

Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms. - Carlos Cotta; Enrique Alba 001; Ramón Sagarna; Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

Mathematical Modeling of Discrete Estimation of Distribution Algorithms. - Cristina González; José Antonio Lozano 001; Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

Feature Subset Selection by Estimation of Distribution Algorithms. - Iñaki Inza; Pedro Larrañaga; Basilio Sierra (Estimation of Distribution Algorithms, 2002)

Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms. - Iñaki Inza; Pedro Larrañaga; Basilio Sierra (Estimation of Distribution Algorithms, 2002)

An Introduction to Probabilistic Graphical Models. - Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

A Review on Estimation of Distribution Algorithms. - Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

Parallel Estimation of Distribution Algorithms. - José Antonio Lozano 001; Ramón Sagarna; Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

Benefits of Data Clustering in Multimodal Function Optimization via EDAs. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

Solving the Traveling Salesman Problem with EDAs. - Víctor Robles; P. de Miguel; Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

An Empirical Comparison Between K-Means, GAs and EDAs in Partitional Clustering. - Josep Roure; Pedro Larrañaga; Ramon Sangüesa (Estimation of Distribution Algorithms, 2002)

Solving the 0-1 Knapsack Problem with EDAs. - Ramón Sagarna; Pedro Larrañaga (Estimation of Distribution Algorithms, 2002)

Rule Induction by Estimation of Distribution Algorithms. - Basilio Sierra; E. A. Jiménez; Iñaki Inza; Pedro Larrañaga; J. Muruzábal (Estimation of Distribution Algorithms, 2002)

Floating Search Methods in Learning Bayesian Networks. - Rosa Blanco; Iñaki Inza; Pedro Larrañaga (Probabilistic Graphical Models, 2002)

Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Probabilistic Graphical Models, 2002)

Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions. - Cristina González; José Antonio Lozano 001; Pedro Larrañaga (Int. J. Approx. Reason., 2002)

Synergies between evolutionary computation and probabilistic graphical models. - Pedro Larrañaga; José Antonio Lozano 001 (Int. J. Approx. Reason., 2002)

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Mach. Learn., 2002)

Inexact graph matching by means of estimation of distribution algorithms. - Endika Bengoetxea; Pedro Larrañaga; Isabelle Bloch; Aymeric Perchant; Claudia Boeres (Pattern Recognit., 2002)

Collaborative Filtering Using Interval Estimation Naïve Bayes. - Víctor Robles; Pedro Larrañaga; José M. Peña 002; Oscar Marbán; F. Javier Crespo; María S. Pérez 001 (AWIC, 2003)

Learning Semi Naïve Bayes Structures by Estimation of Distribution Algorithms. - Víctor Robles; Pedro Larrañaga; José M. Peña 002; María S. Pérez 001; Ernestina Menasalvas Ruiz; Vanessa Herves (EPIA, 2003)

Interval Estimation Naïve Bayes. - Víctor Robles; Pedro Larrañaga; José M. Peña 002; Ernestina Menasalvas Ruiz; María S. Pérez 001 (IDA, 2003)

Analysis of the Univariate Marginal Distribution Algorithm Modeled by Markov Chains. - Cristina González; Juan Diego Rodríguez; José Antonio Lozano 001; Pedro Larrañaga (IWANN (1), 2003)

Parallel Stochastic Search for Protein Secondary Structure Prediction. - Víctor Robles; María S. Pérez 001; Vanessa Herves; José M. Peña 002; Pedro Larrañaga (PPAM, 2003)

Improvement of Naïve Bayes Collaborative Filtering Using Interval Estimation. - Víctor Robles; Pedro Larrañaga; Ernestina Menasalvas Ruiz; María S. Pérez 001; Vanessa Herves (Web Intelligence, 2003)

Algoritmos de Estimación de Distribuciones en Problemas de Optimización Combinatoria. - Pedro Larrañaga; José Antonio Lozano 001; Heinz Mühlenbein (Inteligencia Artif., 2003)

Learning Bayesian networks in the space of structures by estimation of distribution algorithms. - Rosa Blanco; Iñaki Inza; Pedro Larrañaga (Int. J. Intell. Syst., 2003)

GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms. - José M. Peña 002; Víctor Robles; Pedro Larrañaga; Vanessa Herves; Francisco Rosales; María S. Pérez 001 (IEA/AIE, 2004)

Selective Classifiers Can Be Too Restrictive: A Case-Study in Oesophageal Cancer. - Rosa Blanco; Linda C. van der Gaag; Iñaki Inza; Pedro Larrañaga (ISBMDA, 2004)

Protein Folding in 2-Dimensional Lattices with Estimation of Distribution Algorithms. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (ISBMDA, 2004)

Filter versus wrapper gene selection approaches in DNA microarray domains. - Iñaki Inza; Pedro Larrañaga; Rosa Blanco; Antonio J. Cerrolaza (Artif. Intell. Medicine, 2004)

Bayesian network multi-classifiers for protein secondary structure prediction. - Víctor Robles; Pedro Larrañaga; José M. Peña 002; Ernestina Menasalvas Ruiz; María S. Pérez 001; Vanessa Herves; Anita Wasilewska (Artif. Intell. Medicine, 2004)

Gene Selection For Cancer Classification Using Wrapper Approaches. - Rosa Blanco; Pedro Larrañaga; Iñaki Inza; Basilio Sierra (Int. J. Pattern Recognit. Artif. Intell., 2004)

Learning Bayesian Networks In The Space Of Orderings With Estimation Of Distribution Algorithms. - Txomin Romero; Pedro Larrañaga; Basilio Sierra (Int. J. Pattern Recognit. Artif. Intell., 2004)

Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2004)

A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models. - Pedro Larrañaga; Iñaki Inza; Jose Luis Flores 001 (Data Analysis and Visualization in Genomics and Proteomics, 2005)

Interactions and dependencies in estimation of distribution algorithms. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (Congress on Evolutionary Computation, 2005)

Discriminative Learning of Bayesian Network Classifiers via the TM Algorithm. - Guzmán Santafé; José Antonio Lozano 001; Pedro Larrañaga (ECSQARU, 2005)

Average Time Complexity of Estimation of Distribution Algorithms. - Cristina González; A. Ramírez; José Antonio Lozano 001; Pedro Larrañaga (IWANN, 2005)

Editorial Introduction Special Issue on Estimation of Distribution Algorithms. - Pedro Larrañaga; José Antonio Lozano 001 (Evol. Comput., 2005)

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. - José M. Peña 001; José Antonio Lozano 001; Pedro Larrañaga (Evol. Comput., 2005)

Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS. - Rosa Blanco; Iñaki Inza; Marisa Merino; Jorge Quiroga; Pedro Larrañaga (J. Biomed. Informatics, 2005)

Editorial. - Pedro Larrañaga; José Antonio Lozano 001; José M. Peña 001; Iñaki Inza (Mach. Learn., 2005)

Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms. - Roberto Marcondes Cesar Junior; Endika Bengoetxea; Isabelle Bloch; Pedro Larrañaga (Pattern Recognit., 2005)

Bayesian Classifiers in Optimization: An EDA-like Approach. - Teresa Miquélez; Endika Bengoetxea; Pedro Larrañaga (Towards a New Evolutionary Computation, 2006)

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm. - Victor Robles; José M. Peña 001; Pedro Larrañaga; María S. Pérez 001; Vanessa Herves (Towards a New Evolutionary Computation, 2006)

Information Theory and Classification Error in Probabilistic Classifiers. - Aritz Pérez Martínez; Pedro Larrañaga; Iñaki Inza (Discovery Science, 2006)

Mixtures of Kikuchi Approximations. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (ECML, 2006)

Bayesian Model Averaging of TAN Models for Clustering. - Guzmán Santafé; José Antonio Lozano 001; Pedro Larrañaga (Probabilistic Graphical Models, 2006)

Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains. - Teresa Miquélez; Endika Bengoetxea; Pedro Larrañaga (SEAL, 2006)

Discriminative Learning of Bayesian Network Classifiers. - Guzmán Santafé; José Antonio Lozano 001; Pedro Larrañaga (Inteligencia Artif., 2006)

Machine learning in bioinformatics. - Pedro Larrañaga; Borja Calvo; Roberto Santana 001; Concha Bielza; Josu Galdiano; Iñaki Inza; José Antonio Lozano 001; Rubén Armañanzas; Guzmán Santafé; Aritz Pérez Martínez; Victor Robles (Briefings Bioinform., 2006)

Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes. - Aritz Pérez Martínez; Pedro Larrañaga; Iñaki Inza (Int. J. Approx. Reason., 2006)

Bayesian Model Averaging of Naive Bayes for Clustering. - Guzmán Santafé; José Antonio Lozano 001; Pedro Larrañaga (IEEE Trans. Syst. Man Cybern. Part B, 2006)

Exact Bayesian network learning in estimation of distribution algorithms. - Carlos Echegoyen; José Antonio Lozano 001; Roberto Santana 001; Pedro Larrañaga (IEEE Congress on Evolutionary Computation, 2007)

Discriminative vs. Generative Learning of Bayesian Network Classifiers. - Guzmán Santafé; José Antonio Lozano 001; Pedro Larrañaga (ECSQARU, 2007)

The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (EvoBIO, 2007)

Side chain placement using estimation of distribution algorithms. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (Artif. Intell. Medicine, 2007)

A review of feature selection techniques in bioinformatics. - Yvan Saeys; Iñaki Inza; Pedro Larrañaga (Bioinform., 2007)

A partially supervised classification approach to dominant and recessive human disease gene prediction. - Borja Calvo; Núria López-Bigas; Simon J. Furney; Pedro Larrañaga; José Antonio Lozano 001 (Comput. Methods Programs Biomed., 2007)

Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains. - Teresa Miquélez; Endika Bengoetxea; Alexander Mendiburu; Pedro Larrañaga (Connect. Sci., 2007)

Wrapper discretization by means of estimation of distribution algorithms. - Jose Luis Flores 001; Iñaki Inza; Pedro Larrañaga (Intell. Data Anal., 2007)

Learning Bayesian classifiers from positive and unlabeled examples. - Borja Calvo; Pedro Larrañaga; José Antonio Lozano 001 (Pattern Recognit. Lett., 2007)

Component weighting functions for adaptive search with EDAs. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (IEEE Congress on Evolutionary Computation, 2008)

Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (PPSN, 2008)

A review of estimation of distribution algorithms in bioinformatics. - Rubén Armañanzas; Iñaki Inza; Roberto Santana 001; Yvan Saeys; Jose Luis Flores 001; José Antonio Lozano 001; Yves Van de Peer; Rosa Blanco; Víctor Robles; Concha Bielza; Pedro Larrañaga (BioData Min., 2008)

Selection of human embryos for transfer by Bayesian classifiers. - Dinora Araceli Morales; Endika Bengoetxea; Pedro Larrañaga (Comput. Biol. Medicine, 2008)

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers. - Rubén Armañanzas; Iñaki Inza; Pedro Larrañaga (Comput. Methods Programs Biomed., 2008)

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. - Dinora Araceli Morales; Endika Bengoetxea; Pedro Larrañaga; Miguel García; Yosu Franco; Mónica Fresnada; Marisa Merino (Comput. Methods Programs Biomed., 2008)

Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (J. Heuristics, 2008)

Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering. - Guzmán Santafé; José Antonio Lozano 001; Pedro Larrañaga (J. Comput. Biol., 2008)

Protein Folding in Simplified Models With Estimation of Distribution Algorithms. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (IEEE Trans. Evol. Comput., 2008)

The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks. - Carlos Echegoyen; Roberto Santana 001; José Antonio Lozano 001; Pedro Larrañaga (Linkage in Evolutionary Computation, 2008)

Adaptive Estimation of Distribution Algorithms. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (Adaptive and Multilevel Metaheuristics, 2008)

Mining probabilistic models learned by EDAs in the optimization of multi-objective problems. - Roberto Santana 001; Concha Bielza; José Antonio Lozano 001; Pedro Larrañaga (GECCO, 2009)

Probabilistic Graphical Markov Model Learning: An Adaptive Strategy. - Elva Díaz; Eunice Ponce de León; Pedro Larrañaga; Concha Bielza (MICAI, 2009)

Predicting citation count of Bioinformatics papers within four years of publication. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (Bioinform., 2009)

Bayesian classifiers based on kernel density estimation: Flexible classifiers. - Aritz Pérez Martínez; Pedro Larrañaga; Iñaki Inza (Int. J. Approx. Reason., 2009)

Triangulation of Bayesian networks with recursive estimation of distribution algorithms. - Txomin Romero; Pedro Larrañaga (Int. J. Approx. Reason., 2009)

Research topics in discrete estimation of distribution algorithms based on factorizations. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (Memetic Comput., 2009)

Feature subset selection from positive and unlabelled examples. - Borja Calvo; Pedro Larrañaga; José Antonio Lozano 001 (Pattern Recognit. Lett., 2009)

Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models. - José Antonio Lozano 001; Qingfu Zhang 001; Pedro Larrañaga (IEEE Trans. Evol. Comput., 2009)

Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures. - Rubén Armañanzas; Borja Calvo; Iñaki Inza; Marcos López-Hoyos; Víctor Martínez-Taboada; Eduardo Ucar; Irantzu Bernales; Asier Fullaondo; Pedro Larrañaga; Ana M. Zubiaga (IEEE Trans. Inf. Technol. Biomed., 2009)

EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization. - Endika Bengoetxea; Pedro Larrañaga (ANTS Conference, 2010)

Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms. - Alfredo Cuesta-Infante; Roberto Santana 001; José Ignacio Hidalgo; Concha Bielza; Pedro Larrañaga (IEEE Congress on Evolutionary Computation, 2010)

Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (EvoBIO, 2010)

Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances. - Hanen Borchani; Pedro Larrañaga; Concha Bielza (IEA/AIE (1), 2010)

Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (IEA/AIE (3), 2010)

Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation. - Roberto Santana 001; Pedro Larrañaga; José Antonio Lozano 001 (Evol. Comput., 2010)

Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables. - Concha Bielza; Juan A. Fernández del Pozo; Pedro Larrañaga; Endika Bengoetxea (Expert Syst. Appl., 2010)

Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (IEEE Trans. Syst. Man Cybern. Part B, 2010)

The von Mises Naive Bayes Classifier for Angular Data. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (CAEPIA, 2011)

Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables. - Hossein Karshenas; Roberto Santana 001; Concha Bielza; Pedro Larrañaga (EMO, 2011)

Affinity propagation enhanced by estimation of distribution algorithms. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (GECCO, 2011)

Regularized k-order markov models in EDAs. - Roberto Santana 001; Hossein Karshenas; Concha Bielza; Pedro Larrañaga (GECCO, 2011)

Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods. - Roberto Santana 001; Hossein Karshenas; Concha Bielza; Pedro Larrañaga (GECCO (Companion), 2011)

Bayesian Chain Classifiers for Multidimensional Classification. - Julio H. Zaragoza; Luis Enrique Sucar; Eduardo F. Morales 001; Concha Bielza; Pedro Larrañaga (IJCAI, 2011)

Predicting the h-index with cost-sensitive naive Bayes. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (ISDA, 2011)

Probabilistic graphical models in artificial intelligence. - Pedro Larrañaga; Serafín Moral (Appl. Soft Comput., 2011)

Regularized logistic regression without a penalty term: An application to cancer classification with microarray data. - Concha Bielza; Víctor Robles; Pedro Larrañaga (Expert Syst. Appl., 2011)

Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms. - Endika Bengoetxea; Pedro Larrañaga; Concha Bielza; Juan A. Fernández del Pozo (J. Heuristics, 2011)

Classifying evolving data streams with partially labeled data. - Hanen Borchani; Pedro Larrañaga; Concha Bielza (Intell. Data Anal., 2011)

Multi-dimensional classification with Bayesian networks. - Concha Bielza; Guangdi Li; Pedro Larrañaga (Int. J. Approx. Reason., 2011)

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga; Ruth Benavides-Piccione; Javier DeFelipe (Neuroinformatics, 2011)

Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (Neuroinformatics, 2011)

Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (Scientometrics, 2011)

Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms. - Rubén Armañanzas; Yvan Saeys; Iñaki Inza; Miguel García-Torres; Concha Bielza; Yves Van de Peer; Pedro Larrañaga (IEEE ACM Trans. Comput. Biol. Bioinform., 2011)

Maximizing the number of polychronous groups in spiking networks. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (GECCO (Companion), 2012)

Regularized logistic regression and multiobjective variable selection for classifying MEG data. - Roberto Santana 001; Concha Bielza; Pedro Larrañaga (Biol. Cybern., 2012)

Ensemble transcript interaction networks: A case study on Alzheimer's disease. - Rubén Armañanzas; Pedro Larrañaga; Concha Bielza (Comput. Methods Programs Biomed., 2012)

Lazy lasso for local regression. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Comput. Stat., 2012)

A review on probabilistic graphical models in evolutionary computation. - Pedro Larrañaga; Hossein Karshenas; Concha Bielza; Roberto Santana 001 (J. Heuristics, 2012)

A comparison of clustering quality indices using outliers and noise. - Luis Guerra; Víctor Robles; Concha Bielza; Pedro Larrañaga (Intell. Data Anal., 2012)

Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5 Dimensions (EQ-5D) from the 39-item Parkinson's Disease Questionnaire (PDQ-39). - Hanen Borchani; Concha Bielza; Pablo Martínez-Martín; Pedro Larrañaga (J. Biomed. Informatics, 2012)

Wrapper positive Bayesian network classifiers. - Borja Calvo; Iñaki Inza; Pedro Larrañaga; José Antonio Lozano 001 (Knowl. Inf. Syst., 2012)

Forward stagewise naïve Bayes. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Prog. Artif. Intell., 2012)

Semi-supervised Projected Clustering for Classifying GABAergic Interneurons. - Luis Guerra; Ruth Benavides-Piccione; Concha Bielza; Víctor Robles; Javier DeFelipe; Pedro Larrañaga (AIME, 2013)

Learning Conditional Linear Gaussian Classifiers with Probabilistic Class Labels. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (CAEPIA, 2013)

Learning Mixtures of Polynomials of Conditional Densities from Data. - Pedro L. López-Cruz; Thomas D. Nielsen; Concha Bielza; Pedro Larrañaga (CAEPIA, 2013)

Augmented Semi-naive Bayes Classifier. - Bojan Mihaljevic; Pedro Larrañaga; Concha Bielza (CAEPIA, 2013)

Bayesian networks to answer challenging neuroscience questions. - Pedro Larrañaga; Concha Bielza (CBMS, 2013)

Towards optimal neuronal wiring through estimation of distribution algorithms. - Laura Anton-Sanchez; Concha Bielza; Pedro Larrañaga (GECCO (Companion), 2013)

Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach. - Rubén Armañanzas; Concha Bielza; Kallol Ray Chaudhuri; Pablo Martínez-Martín; Pedro Larrañaga (Artif. Intell. Medicine, 2013)

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. - Hanen Borchani; Concha Bielza; Carlos Toro 003; Pedro Larrañaga (Artif. Intell. Medicine, 2013)

Regularized continuous estimation of distribution algorithms. - Hossein Karshenas; Roberto Santana 001; Concha Bielza; Pedro Larrañaga (Appl. Soft Comput., 2013)

A new measure for gene expression biclustering based on non-parametric correlation. - Jose Luis Flores 001; Iñaki Inza; Pedro Larrañaga; Borja Calvo (Comput. Methods Programs Biomed., 2013)

Combinatorial Optimization by Learning and Simulation of Bayesian Networks - Pedro Larrañaga; Ramon Etxeberria; José Antonio Lozano 001; José M. Peña 001 (CoRR, 2013)

Sparse regularized local regression. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Comput. Stat. Data Anal., 2013)

An L1-Regularized naïVE Bayes-Inspired Classifier for Discarding Redundant and Irrelevant Predictors. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Int. J. Artif. Intell. Tools, 2013)

Network measures for information extraction in evolutionary algorithms. - Roberto Santana 001; Rubén Armañanzas; Concha Bielza; Pedro Larrañaga (Int. J. Comput. Intell. Syst., 2013)

Classification of neural signals from sparse autoregressive features. - Diego Vidaurre; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2013)

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. - Miguel García-Torres; Rubén Armañanzas; Concha Bielza; Pedro Larrañaga (Inf. Sci., 2013)

A review on evolutionary algorithms in Bayesian network learning and inference tasks. - Pedro Larrañaga; Hossein Karshenas; Concha Bielza; Roberto Santana 001 (Inf. Sci., 2013)

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks - A Case Study for the Optimal Ordering of Tables. - Concha Bielza; Juan A. Fernández del Pozo; Pedro Larrañaga (J. Comput. Sci. Technol., 2013)

Bayesian Sparse Partial Least Squares. - Diego Vidaurre; Marcel A. J. van Gerven; Concha Bielza; Pedro Larrañaga; Tom Heskes (Neural Comput., 2013)

Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 2000-2009. - Alfonso Ibáñez; Concha Bielza; Pedro Larrañaga (Scientometrics, 2013)

Cluster methods for assessing research performance: exploring Spanish computer science. - Alfonso Ibáñez; Pedro Larrañaga; Concha Bielza (Scientometrics, 2013)

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (Probabilistic Graphical Models, 2014)

Discrete Bayesian Network Classifiers: A Survey. - Concha Bielza; Pedro Larrañaga (ACM Comput. Surv., 2014)

Semi-supervised projected model-based clustering. - Luis Guerra; Concha Bielza; Víctor Robles; Pedro Larrañaga (Data Min. Knowl. Discov., 2014)

Bayesian networks in neuroscience: a survey. - Concha Bielza; Pedro Larrañaga (Frontiers Comput. Neurosci., 2014)

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. - Bojan Mihaljevic; Concha Bielza; Ruth Benavides-Piccione; Javier DeFelipe; Pedro Larrañaga (Frontiers Comput. Neurosci., 2014)

Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2014)

Bayesian network modeling of the consensus between experts: An application to neuron classification. - Pedro L. López-Cruz; Pedro Larrañaga; Javier DeFelipe; Concha Bielza (Int. J. Approx. Reason., 2014)

Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. - Alfonso Ibáñez; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2014)

Multi-label classification with Bayesian network-based chain classifiers. - Luis Enrique Sucar; Concha Bielza; Eduardo F. Morales 001; Pablo Hernandez-Leal; Julio H. Zaragoza; Pedro Larrañaga (Pattern Recognit. Lett., 2014)

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables. - Hossein Karshenas; Roberto Santana 001; Concha Bielza; Pedro Larrañaga (IEEE Trans. Evol. Comput., 2014)

Multi-Dimensional Classification with Super-Classes. - Jesse Read; Concha Bielza; Pedro Larrañaga (IEEE Trans. Knowl. Data Eng., 2014)

Regularized Multivariate von Mises Distribution. - Luis Rodriguez-Lujan; Concha Bielza; Pedro Larrañaga (CAEPIA, 2015)

Towards Gaussian Bayesian Network Fusion. - Irene Córdoba-Sánchez; Concha Bielza; Pedro Larrañaga (ECSQARU, 2015)

Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control. - Javier Díaz; Concha Bielza; Jose L. Ocaña; Pedro Larrañaga (ML4CPS, 2015)

Classifying GABAergic interneurons with semi-supervised projected model-based clustering. - Bojan Mihaljevic; Ruth Benavides-Piccione; Luis Guerra; Javier DeFelipe; Pedro Larrañaga; Concha Bielza (Artif. Intell. Medicine, 2015)

Interval-based ranking in noisy evolutionary multi-objective optimization. - Hossein Karshenas; Concha Bielza; Pedro Larrañaga (Comput. Optim. Appl., 2015)

Conditional Density Approximations with Mixtures of Polynomials. - Gherardo Varando; Pedro L. López-Cruz; Thomas D. Nielsen; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2015)

Decision boundary for discrete Bayesian network classifiers. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (J. Mach. Learn. Res., 2015)

Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons. - Bojan Mihaljevic; Ruth Benavides-Piccione; Concha Bielza; Javier DeFelipe; Pedro Larrañaga (Neuroinformatics, 2015)

Directional naive Bayes classifiers. - Pedro L. López-Cruz; Concha Bielza; Pedro Larrañaga (Pattern Anal. Appl., 2015)

A survey on multi-output regression. - Hanen Borchani; Gherardo Varando; Concha Bielza; Pedro Larrañaga (WIREs Data Mining Knowl. Discov., 2015)

Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions. - Ignacio Leguey; Concha Bielza; Pedro Larrañaga (CAEPIA, 2016)

Hybrid Gaussian and von Mises Model-Based Clustering. - Sergio Luengo-Sanchez; Concha Bielza; Pedro Larrañaga (ECAI, 2016)

Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment. - Alberto Ogbechie; Javier Diaz-Rozo; Pedro Larrañaga; Concha Bielza (ML4CPS, 2016)

Learning Tractable Multidimensional Bayesian Network Classifiers. - Marco Benjumeda; Concha Bielza; Pedro Larrañaga (Probabilistic Graphical Models, 2016)

Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot. - David Atienza; Concha Bielza; Javier Díaz; Pedro Larrañaga (STAIRS, 2016)

A review of undirected and acyclic directed Gaussian Markov model selection and estimation. - Irene Córdoba-Sánchez; Concha Bielza; Pedro Larrañaga (CoRR, 2016)

Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. - Hanen Borchani; Pedro Larrañaga; João Gama 001; Concha Bielza (Intell. Data Anal., 2016)

Decision functions for chain classifiers based on Bayesian networks for multi-label classification. - Gherardo Varando; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2016)

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices. - Alfonso Ibáñez; Rubén Armañanzas; Concha Bielza; Pedro Larrañaga (J. Assoc. Inf. Sci. Technol., 2016)

Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons. - Laura Anton-Sanchez; Concha Bielza; Ruth Benavides-Piccione; Javier DeFelipe; Pedro Larrañaga (Neuroinformatics, 2016)

Learning Bayesian networks with low inference complexity. - Marco Benjumeda; Pedro Larrañaga; Concha Bielza (Prog. Artif. Intell., 2016)

Architecture for anomaly detection in a laser heating surface process. - Javier Mesonero; Concha Bielza; Pedro Larrañaga (ETFA, 2017)

Network design through forests with degree- and role-constrained minimum spanning trees. - Laura Anton-Sanchez; Concha Bielza; Pedro Larrañaga (J. Heuristics, 2017)

Frobenius Norm Regularization for the Multivariate Von Mises Distribution. - Luis Rodriguez-Lujan; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2017)

Univariate and bivariate truncated von Mises distributions. - Pablo Fernandez-Gonzalez; Concha Bielza; Pedro Larrañaga (Prog. Artif. Intell., 2017)

Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks. - Irene Córdoba; Eduardo C. Garrido-Merchán; Daniel Hernández-Lobato; Concha Bielza; Pedro Larrañaga (CAEPIA, 2018)

Asymmetric Hidden Markov Models with Continuous Variables. - Carlos Puerto-Santana; Concha Bielza; Pedro Larrañaga (CAEPIA, 2018)

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (IDEAL (1), 2018)

Multi-dimensional Bayesian Network Classifier Trees. - Santiago Gil-Begue; Pedro Larrañaga; Concha Bielza (IDEAL (1), 2018)

A partial orthogonalization method for simulating covariance and concentration graph matrices. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (PGM, 2018)

Learning Bayesian network classifiers with completed partially directed acyclic graphs. - Bojan Mihaljevic; Concha Bielza; Pedro Larrañaga (PGM, 2018)

Discrete model-based clustering with overlapping subsets of attributes. - Fernando Rodriguez-Sanchez; Pedro Larrañaga; Concha Bielza (PGM, 2018)

Towards a supervised classification of neocortical interneuron morphologies. - Bojan Mihaljevic; Pedro Larrañaga; Ruth Benavides-Piccione; Sean L. Hill; Javier DeFelipe; Concha Bielza (BMC Bioinform., 2018)

Markov Property in Generative Classifiers. - Gherardo Varando; Concha Bielza; Pedro Larrañaga; Eva Riccomagno (CoRR, 2018)

Tractability of most probable explanations in multidimensional Bayesian network classifiers. - Marco Benjumeda; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2018)

Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes. - Javier Diaz-Rozo; Concha Bielza; Pedro Larrañaga (IEEE Internet Things J., 2018)

A regularity index for dendrites - local statistics of a neuron's input space. - Laura Anton-Sanchez; Felix Effenberger; Concha Bielza; Pedro Larrañaga; Hermann Cuntz (PLoS Comput. Biol., 2018)

3D morphology-based clustering and simulation of human pyramidal cell dendritic spines. - Sergio Luengo-Sanchez; Isabel Fernaud; Concha Bielza; Ruth Benavides-Piccione; Pedro Larrañaga; Javier DeFelipe (PLoS Comput. Biol., 2018)

bnclassify: Learning Bayesian Network Classifiers. - Bojan Mihaljevic; Concha Bielza; Pedro Larrañaga (R J., 2018)

Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors. - Pablo Fernandez-Gonzalez; Concepcion Bielza; Pedro Larrañaga (IEEE Access, 2019)

A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas. - Sergio Luengo-Sanchez; Pedro Larrañaga; Concha Bielza (IEEE Access, 2019)

Learning tractable Bayesian networks in the space of elimination orders. - Marco Benjumeda; Concha Bielza; Pedro Larrañaga (Artif. Intell., 2019)

Circular Bayesian classifiers using wrapped Cauchy distributions. - Ignacio Leguey; Concha Bielza; Pedro Larrañaga (Data Knowl. Eng., 2019)

A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks. - Ignacio Leguey; Pedro Larrañaga; Concha Bielza; Shogo Kato (Inf. Sci., 2019)

Tractable learning of Bayesian networks from partially observed data. - Marco Benjumeda; Sergio Luengo-Sanchez; Pedro Larrañaga; Concha Bielza (Pattern Recognit., 2019)

BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks. - Nikolas Bernaola; Mario Michiels; Concha Bielza; Pedro Larrañaga (PGM, 2020)

Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data. - Irene Córdoba; Concha Bielza; Pedro Larrañaga; Gherardo Varando (IEEE Access, 2020)

Incremental Learning of Latent Forests. - Fernando Rodriguez-Sanchez; Pedro Larrañaga; Concha Bielza (IEEE Access, 2020)

Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. - Javier Diaz-Rozo; Concha Bielza; Pedro Larrañaga (Eng. Appl. Artif. Intell., 2020)

On generating random Gaussian graphical models. - Irene Córdoba; Gherardo Varando; Concha Bielza; Pedro Larrañaga (Int. J. Approx. Reason., 2020)

Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem. - Vicente P. Soloviev; Concha Bielza; Pedro Larrañaga (CEC, 2021)

Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding. - David Quesada; Concha Bielza; Pedro Larrañaga (HAIS, 2021)

An Online Feature Selection Methodology for Ball-Bearing Harmonic Frequencies Based on HMMs. - Carlos Puerto-Santana; Pedro Larrañaga; Javier Diaz-Rozo; Concha Bielza (SOCO, 2021)

Multi-dimensional Bayesian network classifiers: A survey. - Santiago Gil-Begue; Concha Bielza; Pedro Larrañaga (Artif. Intell. Rev., 2021)

Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks. - David Quesada; Gabriel Valverde; Pedro Larrañaga; Concha Bielza (Eng. Appl. Artif. Intell., 2021)

Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks. - Bojan Mihaljevic; Pedro Larrañaga; Concha Bielza (Frontiers Neuroinformatics, 2021)

Multidimensional continuous time Bayesian network classifiers. - Carlos Villa-Blanco; Pedro Larrañaga; Concha Bielza (Int. J. Intell. Syst., 2021)

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience. - Mario Michiels; Pedro Larrañaga; Concha Bielza (Neurocomputing, 2021)

Bayesian networks for interpretable machine learning and optimization. - Bojan Mihaljevic; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2021)

Quantum parametric circuit optimization with estimation of distribution algorithms. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (GECCO Companion, 2022)

Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection. - Jorge Casajús-Setién; Concha Bielza; Pedro Larrañaga (PGM, 2022)

Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling. - Enrique Valero-Leal; Pedro Larrañaga; Concha Bielza (PGM, 2022)

Structure learning algorithms for multidimensional continuous-time Bayesian network classifiers. - Carlos Villa-Blanco; Alessandro Bregoli; Concha Bielza; Pedro Larrañaga; Fabio Stella (PGM, 2022)

Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks. - David Quesada; Concha Bielza; Pedro Fontán; Pedro Larrañaga (Int. J. Intell. Syst., 2022)

Multipartition clustering of mixed data with Bayesian networks. - Fernando Rodriguez-Sanchez; Concha Bielza; Pedro Larrañaga (Int. J. Intell. Syst., 2022)

PyBNesian: An extensible python package for Bayesian networks. - David Atienza; Concha Bielza; Pedro Larrañaga (Neurocomputing, 2022)

Asymmetric HMMs for Online Ball-Bearing Health Assessments. - Carlos Puerto-Santana; Concha Bielza; Javier Diaz-Rozo; Guillem Ramirez-Gargallo; Filippo Mantovani; Gaizka Virumbrales; Jesús Labarta; Pedro Larrañaga (IEEE Internet Things J., 2022)

Semiparametric Bayesian networks. - David Atienza; Concha Bielza; Pedro Larrañaga (Inf. Sci., 2022)

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (J. Comb. Optim., 2022)

Autoregressive Asymmetric Linear Gaussian Hidden Markov Models. - Carlos Puerto-Santana; Pedro Larrañaga; Concha Bielza (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Anomaly Detection with Laser Heat Treatment Thermal Videos. - David Atienza; Concha Bielza; Javier Diaz-Rozo; Pedro Larrañaga (IEEE DataPort, 2022)

Variational Quantum Algorithm Parameter Tuning with Estimation of Distribution Algorithms. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (CEC, 2023)

High-Dimensional Feature Characterization of Single Nucleotide Variants in Hypertrophic Cardiomyopathy. - Dafne Lozano-Paredes; Luis Bote; Concha Bielza; Pedro Larrañaga; María Sabater-Molina; Juan Ramón Gimeno; Sergio Muñoz; Francisco Javier Gimeno-Blanes; José Luis Rojo-Álvarez (CinC, 2023)

Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models. - Jorge Casajús-Setién; Concha Bielza; Pedro Larrañaga (CSR, 2023)

Context-specific kernel-based hidden Markov model for time series analysis. - Carlos Puerto-Santana; Concha Bielza; Pedro Larrañaga; Gustav Eje Henter (CoRR, 2023)

Classifying the evolution of COVID-19 severity on patients with combined dynamic Bayesian networks and neural networks. - David Quesada; Pedro Larrañaga; Concha Bielza (CoRR, 2023)

Causal reinforcement learning based on Bayesian networks applied to industrial settings. - Gabriel Valverde; David Quesada; Pedro Larrañaga; Concha Bielza (Eng. Appl. Artif. Intell., 2023)

Quantum approximate optimization algorithm for Bayesian network structure learning. - Vicente P. Soloviev; Concha Bielza; Pedro Larrañaga (Quantum Inf. Process., 2023)

Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers. - Carlos Villa-Blanco; Alessandro Bregoli; Concha Bielza; Pedro Larrañaga; Fabio Stella (Int. J. Approx. Reason., 2023)

Efficient search for relevance explanations using MAP-independence in Bayesian networks. - Enrique Valero-Leal; Concha Bielza; Pedro Larrañaga; Silja Renooij (Int. J. Approx. Reason., 2023)

Feature subset selection for data and feature streams: a review. - Carlos Villa-Blanco; Concha Bielza; Pedro Larrañaga (Artif. Intell. Rev., 2023)

Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection. - Carlos Puerto-Santana; Pedro Larrañaga; Concha Bielza (Neurocomputing, 2023)

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks. - Nikolas Bernaola; Mario Michiels; Pedro Larrañaga; Concha Bielza (PLoS Comput. Biol., 2023)

Multi-objective Counterfactuals in Bayesian Classifiers with Estimation of Distribution Algorithms. - Daniel Zaragoza-Pellicer; Concha Bielza; Pedro Larrañaga (PGM, 2024)

EDAspy: An extensible python package for estimation of distribution algorithms. - Vicente P. Soloviev; Pedro Larrañaga; Concha Bielza (Neurocomputing, 2024)

Feature Saliencies in Asymmetric Hidden Markov Models. - Carlos Puerto-Santana; Pedro Larrañaga; Concha Bielza (IEEE Trans. Neural Networks Learn. Syst., 2024)

Semiparametric Estimation of Distribution Algorithms for Continuous Optimization. - Vicente P. Soloviev; Concha Bielza; Pedro Larrañaga (IEEE Trans. Evol. Comput., 2024)

Estimation of Distribution Algorithms in Machine Learning: A Survey. - Pedro Larrañaga; Concha Bielza (IEEE Trans. Evol. Comput., 2024)

Bandwidth Selectors on Semiparametric Bayesian Networks. - Victor Alejandre; Concha Bielza; Pedro Larrañaga (CoRR, 2025)

Binned semiparametric Bayesian networks. - Rafael Sojo; Javier Diaz-Rozo; Concha Bielza; Pedro Larrañaga (CoRR, 2025)

Actionable Counterfactual Explanations Using Bayesian Networks and Path Planning with Applications to Environmental Quality Improvement. - Enrique Valero-Leal; Pedro Larrañaga; Concha Bielza (CoRR, 2025)