Pedro Larranaga is Full Professor in Computer Science and Artificial Intelligence at the Technical University of Madrid (UPM) since 2007. He received the MSc degree in mathematics (statistics) from the University of Valladolid and the PhD degree in computer science from the University of the Basque Country (“excellence award”). Before moving to UPM, his academic career has been developed at the University of the Basque Country (UPV-EHU) at several faculty ranks: Assistant Professor (1985-1998), Associate Professor (1998-2004) and Full Professor (2004-2007). He earned the habilitation qualification for Full Professor in 2003.

His research interests are primarily in the areas of probabilistic graphical models, metaheuristics for optimization, data mining, classification models, and real applications, like biomedicine, bioinformatics, neuroscience, industry 4.0 and sports. He has published more than 200 papers in impact factor journals and has supervised 30 PhD theses. He is fellow of the European Association for Artificial Intelligence since 2012 and fellow of the Academia Europaea since 2018. He has been awarded the 2013 Spanish National Prize in Computer Science and the prize of the Spanish Association for Artificial Intelligence in 2018. In 2020 he will receive the Amity Research Award in Machine Learning in New Delhi

Google Scholar Profile


  • C. Villa-Blanco, C. Bielza, P. Larrañaga (2022). Feature subset selection for data and feature streams: A review. Artificial Intelligence Review, in press.



  • Córdoba-Sánchez, I., C. Bielza, P. Larrañaga, and G. Varando, “Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data”, IEEE Access, vol. 8, pp. 154614 – 154624, 2020. 
  • Gil-Begue, S., C. Bielza, and P. Larrañaga, “Multi-dimensional Bayesian network classifiers: A survey”, Artificial Intelligence Review, vol. accepted, 2020. BibTex
  • Michiels, M., P. Larrañaga, and C. Bielza, “BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience”, Neurocomputing, vol. 428, pp. 166 – 181, 2020. BibTex


  • Benjumeda, M., C. Bielza, and P. Larrañaga, “Learning tractable Bayesian networks in the space of elimination orders”, Artificial Intelligence, vol. 274, pp. 66-90, 2019. 
  • Benjumeda, M., S.. Luengo-Sanchez, P. Larrañaga, and C. Bielza, “Tractable learning of Bayesian networks from partially observed data”, Pattern Recognition, vol. 91, pp. 190-199, 2019. 
  • Fernandez, P., C. Bielza, and P.. Larrañaga, Random forests for regression as a weighted sum of k-potential nearest neighbors”, IEEE Access, vol. 7, issue 1, pp. 25660-25672, 2019. 
  • Leguey, I., C.. Bielza, and P. Larrañaga, “Circular Bayesian classifiers using wrapped Cauchy distributions”, Data & Knowledge Engineering, vol. 122, pp. 101-115, 2019. 
  • Leguey, I., P. Larrañaga, C. Bielza, and S. Kato, “A circular-linear dependence measure under Johnson–Wehrly distributions and its application in Bayesian networks.”, Information Sciences, vol. 486, pp. 240-253, 2019. 
  • Luengo-Sanchez, S., P. Larrañaga, and C. Bielza, “A directional-linear Bayesian network and its application for clustering and simulation of neural somas”, IEEE Access, vol. 7, issue 1, pp. 69907-69921, 2019. 
  • Mihaljevic, B., R. Benavides-Piccione, C. Bielza, P.. Larrañaga, and J. DeFelipe, “Classification of GABAergic interneurons by leading neuroscientists”, Scientific Data , vol. 6, pp. 221, 2019. 


  • Anton-Sanchez, L., F..Effenberger, C. Bielza, P. Larrañaga, and H.. Cuntz, “A regularity index for dendrites – local statistics of a neuron’s input space”, PLoS Computational Biology, accepted, 2018. 
  • Benjumeda, M., C. Bielza, and P. Larrañaga, “Tractability of Most Probable Explanations in Multidimensional Bayesian Network Classifiers”, International Journal of Approximate Reasoning, vol. 93, pp. 74-87, 2018. 
  • Díaz-Rozo, J., C. Bielza, and P. Larrañaga, “Clustering of Data Streams with Dynamic Gaussian Mixture Models. An IoT Application in Industrial Processes”, IEEE Internet of Things Journal, accepted, 2018. 
  • Luengo-Sanchez, S., I. Fernaud-Espinosa, C. Bielza, R. Benavides-Piccione, P. Larrañaga, and J. DeFelipe, “3D morphology-based clustering and simulation of human pyramidal cell dendritic spines”, PLOS Computational Biology, vol. 14, issue 6, 2018. 
  • Mihaljevic, B., P. Larrañaga, R. Benavides-Piccione, S.. Hill, J.. DeFelipe, and C. Bielza, “Towards a supervised classification of neocortical interneuron morphologies”, BMC Bioinformatics, accepted, 2018. 
  • Varando, G., R. Benavides-Piccione, A. Muñoz, A. Kastanauskaite, C. Bielza, P. Larrañaga, and J. DeFelipe, “MultiMap: A tool to automatically extract and analyze spatial microscopic data from large stacks of confocal microscopy images”, Frontiers in Neuroanatomy, 2018. 


  • Anton-Sanchez, L., P. Larrañaga, R. Benavides-Piccione, I. Fernaud, J. DeFelipe, and C. Bielza, “Three-dimensional spatial modeling of spines along dendritic networks in human cortical pyramidal neurons”, PLoS ONE 12(6): e0180400, 2017. 
  • Anton-Sanchez, L., C. Bielza, and P. Larrañaga, “Network Design through Forests with Degree- and Role-constrained Minimum Spanning Trees”, Journal of Heuristics, vol. 23, issue 1, pp. 31-51, 2017. 
  • Díaz-Rozo, J., C. Bielza, and P.. Larrañaga, “Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions”, Procedia Manufacturing Elsevier (no JCR), vol. 10, pp. 997-1008, 2017. 
  • Fernandez-Gonzalez, P., R. Benavides-Piccione, I. Leguey, C. Bielza, P. Larrañaga, and J. DeFelipe, “Dendritic branching angles of pyramidal neurons of the human cerebral cortex”, Brain Structure and Function, vol. 222, issue 4, pp. 1847-1859, 2017. 
  • Fernandez-Gonzalez, P., C. Bielza, and P. Larrañaga, “Univariate and bivariate truncated von Mises distributions”, Progress in Artificial Intelligence (no JCR), pp. 1-10, 2017. 
  • Mu, J., K. R. Chaudhuri, C. Bielza, D. Pedro J., P. Larrañaga, and P. Martínez-Martín, “Parkinson’s Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms”, Frontiers in Aging Neuroscience, vol. 9, 20/09/2017. 
  • Rodriguez-Lujan, L., P. Larrañaga, and C. Bielza, “Frobenius norm regularization for the multivariate von Mises distribution”, International Journal of Intelligent Systems, vol. 32, issue 2, pp. 153–176, 2017. 


  • Anton-Sanchez, L., C. Bielza, R. Benavides-Piccione, J. DeFelipe, and P. Larrañaga, “Dendritic and axonal wiring optimization of cortical GABAergic interneurons”, Neuroinformatics, vol. 14, issue 4, pp. 453-464, 2016. 
  • Anton-Sanchez, L., C. Bielza, P. Larrañaga, and J. DeFelipe, “Wiring Economy of Pyramidal Cells in the Juvenile Rat Somatosensory Cortex”, PLoS ONE, vol. 11, issue 11, 2016. 
  • Benjumeda, M., C. Bielza, and P. Larrañaga, “Learning Bayesian networks with low inference complexity”, Progress in Artificial Intelligence (no JCR), vol. 5, issue 1, pp. 15-26, 2016. 
  • Borchani, H., P. Larrañaga, J. Gama, and C. Bielza, “Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers”, Intelligent Data Analysis, vol. 20, no. 2, 2016. 
  • Leguey, I., C. Bielza, P. Larrañaga, A. Kastanauskaite, C. Rojo, R. Benavides-Piccione, and J. DeFelipe, “Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortex”, Journal of Comparative Neurology, vol. 524, issue 13, pp. 2567-2576, 2016. 
  • Leitner, L., C. Bielza, S. L. Hill, and P. Larrañaga, “Data Publications Correlate with Citation Impact”, Frontiers in Neuroscience, vol. 10, issue 419, 2016. 
  • Rojo, C., I. Leguey, A. Kastanauskaite, C. Bielza, P.. Larrañaga, J.. DeFelipe, and R.. Benavides-Piccione, “Laminar differences in dendritic structure of pyramidal neurons in juvenile rat somatosensory cortex”, Cerebral Cortex, vol. 26, issue 6, pp. 2811-2822, 2016. 
  • Varando, G., C. Bielza, and P. Larrañaga, “Decision Functions for Chain Classifiers based on Bayesian Networks for Multi-Label Classification”, International Journal of Approximate Reasoning, vol. 68, pp. 164-178, 2016. 


  • Borchani, H., G. Varando, C. Bielza, and P. Larrañaga, “A survey on multi-output regression”, WIREs Data Mining and Knowledge Discovery, vol. 5, pp. 216–233, 2015. 
  • Ibañez, A., R. Armañanzas, C. Bielza, and P. Larrañaga, “Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices”, Journal of the American Society for Information Science and Technology, vol. 67, issue 7, pp. 1703–1721, 2015. 
  • Karshenas, H., C. Bielza, and P. Larrañaga, “Interval-based ranking in noisy evolutionary multi-objective optimization”, Computational Optimization and Applications, vol. 61, issue 2, pp. 517-555, 2015. 
  • Larrañaga, A., C. Bielza, P. Pongrácz, T. Faragó, P. Bálint, and P. Larrañaga, “Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking”, Animal Cognition, vol. 18, no. 2, pp. 405-421, 2015. 
  • Lopez-Cruz, P. L., C. Bielza, and P. Larrañaga, “Directional naive Bayes classifiers”, Pattern Analysis and Applications, vol. 18, pp. 225-246, 2015. 
  • Luengo-Sanchez, S., C. Bielza, R. Benavides-Piccione, I. Fernaud-Espinosa, J. DeFelipe, and P. Larrañaga, “A univocal definition of the neuronal soma morphology using Gaussian mixture models”, Frontiers in Neuroanatomy, vol. 9, issue 137, 2015. 
  • Masegosa, A., R. Armañanzas, M. A. Grau, V. Potenciano, S. Moral, P. Larrañaga, C. Bielza, and F. Matesanz, “Discretization of Expression Quantitative Trait Loci in Association Analysis Between Genotypes and Expression Data”, Current Bioinformatics, vol. 10, no. 2, pp. 144-164, 2015. 
  • Mihaljevic, B., R. Benavides-Piccione, C. Bielza, J. DeFelipe, and P. Larrañaga, “Bayesian network classifiers for categorizing cortical GABAergic interneurons”, Neuroinformatics, vol. 13, no. 2, pp. 192–208, April, 2015. 
  • Mihaljevic, B., R. Benavides-Piccione, L. Guerra, J. DeFelipe, P. Larrañaga, and C. Bielza, “Classifying GABAergic interneurons with semi-supervised projected model-based clustering”, Artificial Intelligence in Medicine, vol. 65, issue 1, pp. 49-59, 2015. 
  • Olazarán, J., M. Valentí, B. Frades, M. Ascensión Zea-Sevilla, M. Ávila-Villanueva, M. Ángel Fernández-Blázquez, M. Calero, J. Luis Dobato, J. Antonio Hernández-Tamames, B. León-Salas, L. Agüera-Ortiz, J. López-Álvarez, P. Larrañaga, C. Bielza, J. Álvarez-Linera, and P. Martínez-Martín, “The Vallecas Project: a cohort to identify early markers and mechanisms of Alzheimer’s disease”, Frontiers in Aging Neuroscience, vol. 7, pp. 181, 2015. 
  • Varando, G., P. L. Lopez-Cruz, T. D. Nielsen, P. Larrañaga, and C. Bielza, “Conditional density approximations with mixtures of polynomials”, International Journal of Intelligent Systems, vol. 30, no. 3, pp. 236–264, 2015. 
  • Varando, G., C. Bielza, and P. Larrañaga, “Decision Boundary for Discrete Bayesian Network Classifiers”, Journal of Machine Learning Research, vol. 16, pp. 2725-2749, 2015. 


  • Anton-Sanchez, L., C. Bielza, A. Merchán-Pérez, J. R. Rodríguez, J. DeFelipe, and P. Larrañaga, “Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis”, Frontiers in Neuroanatomy, vol. 8, pp. Article 85, 2014, Also available in the ebook: 
  • Bielza, C., and P. Larrañaga, “Bayesian networks in neuroscience: A survey”, Frontiers in Computational Neuroscience, vol. 8, pp. Article 131, 2014. 
  • Bielza, C., R. Benavides-Piccione, P. L. Lopez-Cruz, P. Larrañaga, and J. DeFelipe, “Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas”, Scientific Reports, vol. 4, pp. Article 5909, 2014. 
  • Bielza, C., and P. Larrañaga, “Discrete Bayesian Network Classifiers: A Survey”, ACM Computing Surveys, vol. 47, no. 1, pp. Article 5, 2014. 
  • Borchani, H., C. Bielza, P. Martínez-Martín, and P. Larrañaga, “Predicting EQ-5D from the Parkinson’s disease questionnaire PDQ-8 using multi-dimensional Bayesian network classifiers”, Biomedical Engineering: Applications, Basis and Communications, vol. 26, no. 1, pp. 1450015, 2014. 
  • Guerra, L., C. Bielza, V. Robles, and P. Larrañaga, “Semi-supervised projected model-based clustering”, Data Mining and Knowledge Discovery, vol. 28, no. 4, pp. 882-917, 2014. 
  • Ibañez, A., C. Bielza, and P. Larrañaga, “Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals”, Neurocomputing, vol. 135, no. 5, pp. 45-52, 2014. 
  • Karshenas, H., R. Santana, C. Bielza, and P. Larrañaga, “Multi-objective estimation of distribution algorithm based on joint modeling of objectives and variables”, IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 519-542, 2014. 
  • Lopez-Cruz, P. L., P. Larrañaga, , and C. Bielza, “Bayesian network modeling of the consensus between experts: An application to neuron classification”, International Journal of Approximate Reasoning, vol. 55, no. 1, pp. 3-22, 2014. 
  • Lopez-Cruz, P. L., C. Bielza, and P. Larrañaga, “Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation”, International Journal of Approximate Reasoning, vol. 55, no. 4, pp. 989–1010, 2014. 
  • Merchán-Pérez, A., R. Rodríguez, S. González, V. Robles, J. DeFelipe, P. Larrañaga, and C. Bielza, “Three-dimensional spatial distribution of synapses in the neocortex: A dual-beam electron microscopy study”, Cerebral Cortex, vol. 24, pp. 1579-1588, 2014. 
  • Mihaljevic, B., C. Bielza, R. Benavides-Piccione, J. DeFelipe, and P. Larrañaga, “Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty”, Frontiers in Computional Neuroscience, vol. 8, pp. Article 150, 2014. 
  • Morales, J., R. Benavides-Piccione, M. Dar, I. Fernaud, A. Rodríguez, L. Anton-Sanchez, C. Bielza, P. Larrañaga, J. DeFelipe, and R. Yuste, “Random positions of dendritic spines in human cerebral cortex”, Journal of Neuroscience, vol. 34, no. 30, pp. 10078-10084, 2014. 
  • Read, J., C. Bielza, and P. Larrañaga, “Multi-dimensional classification with super-classes”, IEEE Transactions on Knowledge and Data Engineering, vol. 26, no. 7, pp. 1720-1733, 2014. 
  • Sucar, E., C. Bielza, E. F. Morales, P. Hernandez-Leal, J. H. Zaragoza, and P. Larrañaga, “Multi-label Classification with Bayesian Network-based Chain Classifiers”, Pattern Recognition Letters, vol. 41, pp. 14-22, 2014. 


  • Armañanzas, R., C. Bielza, K. R. Chaudhuri, P. Martínez-Martín, and P. Larrañaga, “Unveiling relevant non-motor Parkinson’s disease severity symptoms using a machine learning approach”, Artificial Intelligence in Medicine, vol. 58, no. 3, pp. 195-202, 2013. 
  • Armañanzas, R., L. Alonso-Nanclares, J. DeFelipe, A. Kastanauskaite, R. G. de Sola, J. DeFelipe, C. Bielza, and P. Larrañaga, “Machine learning approach for the outcome prediction of temporal lobe epilepsy surgery”, PLoS ONE, vol. 8, no. 4, pp. e62819, 2013. 
  • Bielza, C., J. A. Fernandez del Pozo, and P. Larrañaga, “Parameter control of genetic algorithms by learning and simulation of Bayesian Networks. A case study for the optimal ordering of tables”, Journal of Computer Science and Technology, vol. 28, no. 4, pp. 720-731, 2013. 
  • Borchani, H., C. Bielza, , and P. Larrañaga, “Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers”, Artificial Intelligence in Medicine, vol. 57, no. 3, pp. 219-229, 2013. 
  • DeFelipe, J., P. L. Lopez-Cruz, R. Benavides-Piccione, C. Bielza, P. Larrañaga, and et. al., “New insights into the classification and nomenclature of cortical GABAergic interneurons”, Nature Reviews Neuroscience, vol. 14, no. 3, pp. 202-216, 2013. 
  • Flores, J. L., I. Inza, P. Larrañaga, and B. Calvo, “A new measure for gene expression biclustering based on non-parametric correlation”, Computer Methods and Programs in Biomedicine, vol. 113, no. 3, pp. 367-397, 2013. 
  • García-Torres, M., R. Armañanzas, C. Bielza, and P. Larrañaga, “Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data”, Information Sciences, vol. 222, pp. 229-246, 2013. 
  • Ibañez, A., C. Bielza, and P. Larrañaga, “Relationship among research collaboration, number of documents and number of citations. A case study in Spanish computer science production in 2000-2009”, Scientometrics, vol. 95, no. 2, pp. 689-716, 2013. 
  • Ibañez, A., P. Larrañaga, and C. Bielza, “Cluster methods for assessing research performance: exploring Spanish computer science”, Scientometrics, vol. 97, pp. 571-600, 2013. 
  • Ibañez, A., C. Bielza, and P. Larrañaga, “Análisis de la actividad científica de las universidades públicas españolas en el área de las tecnologías informáticas”, Revista Española de Documentación Científica, vol. 36, no. 1, pp. e002, 2013. 
  • Karshenas, H., R. Santana, C. Bielza, and P. Larrañaga, “Regularized continuous estimation of distribution algorithms”, Applied Soft Computing, vol. 13, no. 5, pp. 2412–2432, 2013. 
  • Larrañaga, P., H. Karshenas, C. Bielza, and R. Santana, “A review on evolutionary algorithms in Bayesian network learning and inference tasks”, Information Sciences, vol. 233, pp. 109-125, 2013. 
  • Morales, D., Y. Vives-Gilabert, B. Gómez-Ansón, E. Bengoetxea, P. Larrañaga, C. Bielza, J. Pagonabarraga, J. Kulisevsky, I. Corcuera-Solano, and M. Delfino, “Predicting dementia development in Parkinson’s disease using Bayesian network classifiers”, Psychiatry Research: NeuroImaging, vol. 213, pp. 92-98, 2013. 
  • Santana, R., R. Armañanzas, C. Bielza, and P. Larrañaga, “Network measures for information extraction in evolutionary algorithms”, International Journal of Computational Intelligence Systems, vol. 6, no. 6, pp. 1163-1188, 2013. 
  • Santana, R., L. McGarry, C. Bielza, P. Larrañaga, and R. Yuste, “Classification of neocortical interneurons using affinity propagation”, Frontiers in Neural Circuits, vol. 7:185, 2013. 
  • Vidaurre, D., M. van Gerven, C. Bielza, P. Larrañaga, and T. Heskes, “Bayesian sparse partial least squares”, Neural Computation, vol. 25, no. 12, pp. 3318–3339, 2013. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “An L1-regularized naive Bayes-inspired classifier for discarding redundant and irrelevant predictors”, International Journal on Artificial Intelligence Tools, vol. 22, no. 4, pp. 1350019, 2013. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “A survey on L1-regression”, International Statistical Review, vol. 81, no. 3, pp. 361-387, 2013. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “Classification of neural signals from sparse autoregressive features”, Neurocomputing, vol. 111, pp. 21-26, 2013. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “Sparse regularized local regression”, Computational Statistics and Data Analysis, vol. 62, pp. 122-135, 2013. 


  • Armañanzas, R., P. Larrañaga, and C. Bielza, “Ensemble transcript interaction networks: A case study on Alzheimer’s disease”, Computer Methods and Programs in Biomedicine, vol. 108, no. 1, pp. 442-450, 2012. 
  • Borchani, H., C. Bielza, P. Martínez-Martín, and P. Larrañaga, “Markov blanket-based approach for learning multi-dimensional Bayesian network classifiers: An application to predict the European Quality of Life-5Dimensions (EQ-5D) from the 39-item Parkinson’s Disease Questionnaire (PDQ-39)”, Journal of Biomedical Informatics, vol. 45, pp. 1175-1184, 2012. 
  • Calvo, B., I. Inza, P. Larrañaga, and J. A. Lozano, “Wrapper positive Bayesian network classifiers”, Knowledge and Information Systems, vol. 33, no. 3, pp. 631-654, 2012. 
  • García-Bilbao, A., R. Armañanzas, Z. Ispizua, B. Calvo, A. Alonso-Varona, I. Inza, P. Larrañaga, G. López-Vivanco, B. Suárez-Merino, and M. Betanzos, “Identification of a biomarker panel for colorectal cancer diagnosis”, BMC Cancer, vol. 12, no. 43, 2012. 
  • Guerra, L., V. Robles, C. Bielza, and P. Larrañaga, “A comparison of clustering quality indices using outliers and noise”, Intelligent Data Analysis, vol. 16, no. 4, pp. 703-715, 2012. 
  • Larrañaga, P., H. Karshenas, C. Bielza, and R. Santana, “A review on probabilistic graphical models in evolutionary computation”, Journal of Heuristics, vol. 18, no. 5, pp. 795-819, 2012. 
  • Santana, R., C. Bielza, and P. Larrañaga, “Regularized logistic regression and multi-objective variable selection for classifying MEG data”, Biological Cybernetics, vol. 106, no. 6-7, pp. 389-405, 2012. 
  • Santana, R., C. Bielza, and P. Larrañaga, “Conductance interaction identification by means of Boltzmann distribution and mutual information analysis in conductance-based neuron models”, BMC Neuroscience, vol. 13, no. Suppl 1, pp. P100, 2012. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “Lazy lasso for local regression”, Computational Statistics, vol. 27, no. 3, pp. 531-550, 2012. 
  • Vidaurre, D., E. E. Rodríguez, C. Bielza, P. Larrañaga, and P. Rudomin, “A new feature extraction method for signal classification applied to cat spinal cord signals”, Journal of Neural Engineering, vol. in press, 2012. 


  • Armañanzas, R., Y. Saeys, I. Inza, M. Garca-Torres, C. Bielza, Y. van de Peer, and P. Larrañaga, “Peakbin selection in mass spectrometry data using a consensus approach with estimation of distribution algorithms”, IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 8, no. 3, pp. 760-774, 2011. 
  • Bengoetxea, E., P. Larrañaga, C. Bielza, and J. A. Fernandez del Pozo, “Optimal Row and Column Ordering to Improve Table Interpretation Using Estimation of Distribution Algorithms”, Journal of Heuristics, vol. 17, no. 5, pp. 567-588, 2011. 
  • Bielza, C., G. Li, and P. Larrañaga, “Multi-Dimensional Classification with Bayesian Networks”, International Journal of Approximate Reasoning, vol. 52, pp. 705-727, 2011. 
  • Bielza, C., V. Robles, and P. Larrañaga, “Regularized Logistic Regression without a Penalty Term: An Application to Cancer Classification with Microarray Data”, Expert Systems with Applications, vol. 38, pp. 5110-5118, 2011. 
  • Borchani, H., P. Larrañaga, and C. Bielza, “Classifying evolving data streams with partially labeled data”, Intelligent Data Analysis, vol. 15, no. 5, pp. 655-670, 2011. 
  • Guerra, L., L. McGarry, V. Robles, C. Bielza, P. Larrañaga, and R. Yuste, “Comparison between Supervised and Unsupervised Classification of Neuronal Cell Types: A Case Study”, Developmental Neurobiology, vol. 71, no. 1, pp. 71-82, 2011. 
  • Ibañez, A., P. Larrañaga, and C. Bielza, “Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals”, Scientometrics, vol. 89, no. 2, pp. 523-551, 2011. 
  • Larrañaga, P., and S. Moral, “Probabilistic graphical models in artificial intelligence”, Applied Soft Computing, vol. 11, no. 2, pp. 1511-1528, 2011. 
  • Lopez-Cruz, P. L., C. Bielza, P. Larrañaga, R. Benavides-Piccione, and J. DeFelipe, “Models and simulation of 3D neuronal dendritic trees using Bayesian networks”, Neuroinformatics, vol. 9, no. 4, pp. 347-369, 2011. 
  • Santana, R., C. Bielza, and P. Larrañaga, “Optimizing brain networks topologies using multi-objective evolutionary computation”, Neuroinformatics, vol. 9, no. 1, pp. 3-19, 2011. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “On nonlinearity in neural encoding models applied to the primary visual cortex”, Network: Computation in Neural Systems, vol. 22, no. 1-4, pp. 97-125, 2011. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “Forward Stagewise Naive Bayes”, Progress in Artificial Intelligence, vol. In press, 2011. 


  • Bielza, C., J. A. Fernandez del Pozo, P. Larrañaga, and E. Bengoetxea, “Multidimensional Statistical Analysis of the Parameterization of a Genetic Algorithm for the Optimal Ordering of Tables”, Expert Systems with Applications, vol. 37, pp. 804-815, 2010. 
  • Cuesta, I., C. Bielza, M. Cuenca-Estrella, P. Larrañaga, and J. L. Rodriguez-Tudela, “Evaluation by Data Mining Techniques of Fluconazole Breakpoints Established by the Clinical and Laboratory Standards Institute (CLSI) and Comparison with those of the European Committee on Antimicrobial Susceptibility Testing (EUCAST)”, Antimicrobial Agents and Chemotherapy, vol. 54, no. 4, pp. 1541-1546, 2010. 
  • Santana, R., C. Bielza, P. Larrañaga, J. A. Lozano, C. Echegoyen, A. Mendiburu, R. Armañanzas, and S. Shakya, “MATEDA-2.0: A Matlab Package for the Implementation and Analysis of Estimation of Distribution Algorithms”, Journal of Statistical Software, vol. 35, no. 7, pp. 1-30, 2010. 
  • Santana, R., P. Larrañaga, and J. A. Lozano, “Learning factorizations in estimation of distribution algorithms using affinity propagation”, Evolutionary Computation, vol. in press, 2010. 
  • Vidaurre, D., C. Bielza, and P. Larrañaga, “Learning an L1-regularized Gaussian Bayesian Network in the Equivalence Class Space”, IEEE Transactions on Systems, Man and Cybernetics, Part B, vol. 40, no. 5, pp. 1231-1242, 2010. 


  • Armañanzas, R., B. Calvo, I. Inza, M. López-Hoyos, V. Martínez-Taboada, E. Ucar, I. Bernales, A. Fullaondo, P. Larrañaga, and A. M. Zubiaga, “Microarray analysis of autoimmune diseases by machine learning procedures”, IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 3, pp. 341-350, 2009. 
  • Bielza, C., V. Robles, and P. Larrañaga, “Estimation of Distribution Algorithms as Logistic Regression Regularizers of Microarray Classifiers”, Methods of Information in Medicine, vol. 48, no. 3, pp. 236-241, 2009. 
  • Cuesta, I., C. Bielza, P. Larrañaga, M. Cuenca-Estrella, F. Laguna, D. Rodríguez-Pardo, B. Almirante, A. Pahissa, and J. L. Rodriguez-Tudela, “Data Mining Validation of EUCAST Fluconazole Breakpoints Established by the European Committee on Antimicrobial Susceptibility Testing”, Antimicrobial Agents and Chemotherapy, vol. 53, no. 7, pp. 2949-2954, 2009. 
  • Ibañez, A., P. Larrañaga, and C. Bielza, “Predicting Citation Count of Bioinformatics Papers within Four Years of Publication”, Bioinformatics, vol. 25, no. 24, pp. 3303-3309, 2009. 
  • Pérez, A., P. Larrañaga, and I. Inza, “Bayesian classifiers based on kernel estimation: Flexible classifiers”, International Journal of Approximate Reasoning, vol. 50, no. 2, pp. 341-362, 2009. 
  • Romero, T., and P. Larrañaga, “Triangulation of Bayesian networks with recursive estimation of distribution algorithms”, International Journal of Approximate Reasoning, vol. 50, no. 3, pp. 472-484, 2009.


  • Armañanzas, R., I. Inza, and P. Larrañaga, “Detecting reliable gene interactions by a hierarchy of Bayesian networks classifiers”, Computer Methods and Programs in Biomedicine, vol. 91, pp. 110-121, 2008. 
  • Furney, S. J., B. Calvo, P. Larrañaga, J. A. Lozano, and N. López-Bigas, “Prioritization of candidate cancer genes-an aid to oncogenomic studies”, Nucleic Acids Research, pp. 1-9, 2008. 
  • Morales, D., E. Bengoetxea, and P. Larrañaga, “Selection of human embryos for transfer by Bayesian classifiers”, Computer in Biology and Medicine, vol. 38, pp. 1177-1186, 2008. 
  • Morales, D., E. Bengoetxea, P. Larrañaga, M. Garca, Y. Franco-Iriarte, M. Fresnada, and M. Merino, “Bayesian classification for the selection of in-vitro human embryos using morphological and clinical data”, Computer Methods and Programs in Biomedicine, no. 90, pp. 104-116, 2008. 
  • Robles, V., C. Bielza, P. Larrañaga, S. González, and L. Ohno-Machado, “Optimizing Logistic Regression Coefficients for Discrimination and Calibration Using Estimation of Distribution Algorithms”, TOP, vol. 16, pp. 345-366, 2008. 
  • Santafé, G., J. A. Lozano, and P. Larrañaga, “Inference of population structure using genetic markers and a Bayesian model averaging approach for clustering”, Journal of Computational Biology, vol. 15, no. 2, pp. 207-220, 2008. 
  • Santana, R., J. A. Lozano, and P. Larrañaga, “Protein folding in simplified models with estimation of distribution algorithms”, IEEE Transactions on Evolutionary Computation, vol. 12, no. 4, pp. 418-438, 2008. 
  • Santana, R., P. Larrañaga, and J. A. Lozano, “Combining variable neighborhood search and estimation of distribution algorithms”, Journal of Heuristics, vol. 14, pp. 519-547, 2008. 
  • Zipritia, I., J. Elorriaga, A. Arruarte, P. Larrañaga, and R. Armañanzas, “What is behind a summary evaluation decision?”, Behavior Research Methods, vol. 2, no. 40, pp. 597-612, 2008. 


  • Calvo, B., N. López-Bigas, S. J. Furney, P. Larrañaga, and J. A. Lozano, “A partially supervised approach to dominant and recessive human disease gene prediction”, Computer Methods and Programs in Biomedicine, vol. 85, no. 3, pp. 229-237, 2007. 
  • Calvo, B., J. A. Lozano, and P. Larrañaga, “Learning Bayesian classifiers from positive and unlabeled examples”, Pattern Recognition Letters, vol. 28, no. 16, pp. 2375-2384, 2007. 
  • Flores, J. L., I. Inza, and P. Larrañaga, “Wrapper discretization by means of estimation of distribution algorithms”, Intelligent Data Analysis Journal, vol. 11, no. 5, pp. 525-546, 2007. 
  • Miquelez, T., E. Bengoetxea, A. Mendiburu, and P. Larrañaga, “Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains”, Connection Science, vol. 19, no. 4, pp. 297-319, 2007. 
  • Saeys, Y., I. Inza, and P. Larrañaga, “A review of feature selection techniques in bioinformatics”, Bioinformatics, vol. 23, no. 19, pp. 2507-2517, 2007. 
  • Santana, R., P. Larrañaga, and J. A. Lozano, “Side chain placement using estimation of distribution algorithms”, Artificial Intelligence in Medicine, vol. 39, no. 1, pp. 49-63, 2007. 


  • Larrañaga, P., B. Calvo, R. Santana, C. Bielza, J. Galdiano, I. Inza, J. A. Lozano, R. Armañanzas, G. Santafé, and A. Pérez, “Machine Learning in Bioinformatics”, Briefings in Bioinformatics, vol. 17, no. 1, pp. 86-112, 2006. 
  • Pérez, A., P. Larrañaga, and I. Inza, “Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes”, International Journal of Approximate Reasoning, vol. 43, pp. 1-25, 2006. 
  • Santafé, G., J. A. Lozano, and P. Larrañaga, “Bayesian model averaging of naive Bayes for clustering”, IEEE Transactions on Systems, Man, and Cybernetics, vol. 36, no. 5, pp. 1149-1161, 2006. 


  • Blanco, R., I. Inza, M. Merino, J. Quiroga, and P. Larrañaga, “Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS”, Journal of Biomedical Informatics, vol. 38, pp. 376–388, 2005. 
  • Larrañaga, P., J. A. Lozano, J. M. Peña, and I. Inza, “Special issue on Probabilistic Graphical Models in Classification”, Machine Learning, vol. 59, pp. 211-212, 2005. 
  • Larrañaga, P., and J. A. Lozano, “Special issue on estimation of distribution algorithms”, Evolutionary Computation, vol. 13, no. 1, pp. v-vi, 2005. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “Globally multimodal problem optimization via an estimation of distribution algorithm based on unsupervised learning of Bayesian networks”, Evolutionary Computation, pp. 43-66, 2005. 
  • Roberto, C., E. Bengoetxea, I. Bloch, and P. Larrañaga, “Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms”, Pattern Recognition, vol. 38, pp. 2099–2113, 2005. 


  • Blanco, R., P. Larrañaga, I. Inza, and B. Sierra, “Gene selection for cancer classification using wrapper approaches”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 8, pp. 1373-1390, 2004. 
  • Inza, I., P. Larrañaga, R. Blanco, and A. J. Cerrolaza, “Filter versus wrapper gene selection approaches in DNA microarray domains”, Artificial Intelligence in Medicine, no. 31, pp. 91-103, 2004. 
  • Larrañaga, P., E. Menasalvas, J. M. Peña, and V. Robles, “Data mining in genomics and proteomics”, Artificial Intelligence in Medicine, no. 31, pp. iii-iv, 2004. 
  • Miquelez, T., E. Bengoetxea, and P. Larrañaga, “Evolutionary computation based on Bayesian classifiers”, International Journal of Applied Mathematics and Computer Science, vol. 14, no. 3, pp. 101–115, 2004. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “Unsupersived learning of Bayesian networks via estimation of distribution algorithms: an application to gene expression data clustering”, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 12, pp. 63-82, 2004.
  • Robles, V., P. Larrañaga, J. M. Peña, E. Menasalvas, M. S. Pérez, and V. Herves, “Bayesian networks as consensed voting system in the construction of a multi–classifier for protein secondary structure prediction”, Artificial Intelligence in Medicine, no. 31, pp. 117–136, 2004. 
  • Romero, T., P. Larrañaga, and B. Sierra, “Learning Bayesian networks in the space of orderings with estimation of distribution algorithms”, International Journal of Pattern Recognition and Artificial Intelligence, vol. 18, no. 4, pp. 607–625, 2004. 


  • Blanco, R., I. Inza, and P. Larrañaga, “Learning Bayesian networks in the space of structures by estimation of distribution algorithms”, International Journal of Intelligent Systems, vol. 18, pp. 205-220, 2003. 


  • Bengoetxea, E., P. Larrañaga, I. Bloch, A. Perchant, and C. Boeres, “Inexact graph matching by means of estimation of distribution algorithms”, Pattern Recognition, vol. 35, no. 12, pp. 2867–2880, 2002. 
  • González, C., J. A. Lozano, and P. Larrañaga, “Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions”, International Journal of Approximate Reasoning, vol. 31, pp. 313–340, 2002. 
  • González, C., J. A. Lozano, and P. Larrañaga, “Modelado matemático del algoritmo UMDAc con selección por torneo aplicado a funciones lineales”, Primer Congreso Español de Algoritmos Evolutivos y Bioinspirados, pp. 437–444, 2002. 
  • Inza, I., B. Sierra, R. Blanco, and P. Larrañaga, “Gene selection by sequential search wrapper approaches in microarray cancer class prediction”, Journal of Intelligent and Fuzzy Systems, vol. 12, no. 1, pp. 25-33, 2002. 
  • Larrañaga, P., and J. A. Lozano, “Synergies between evolutionary computation and probabilistic graphical models”, International Journal of Approximate Reasoning, vol. 31, pp. 155–156, 2002. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “Learning recursive Bayesian multinets for clustering by means of constructive induction”, Machine Learning, vol. 47, pp. 63-89, 2002. 


  • Inza, I., M. Merino, P. Larrañaga, J. Quiroga, B. Sierra, and M. Girala, “Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS”, Artificial Intelligence in Medicine, vol. 23, no. 2, pp. 187–205, 2001. 
  • Inza, I., P. Larrañaga, and B. Sierra, “Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms”, International Journal of Approximate Reasoning, vol. 27, pp. 143–164, 2001. 
  • Peña, J. M., J. A. Lozano, P. Larrañaga, and I. Inza, “Dimensionality reduction in unsupervised learning of conditional Gaussian networks”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 6, pp. 590–603, 2001. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “Performance evaluation of compromise conditional Gaussian networks for data clustering”, International Journal of Approximate Reasoning, vol. 28, pp. 23-50, 2001. 
  • Sierra, B., N. Serrano, P. Larrañaga, E. J. Plasencia, I. Inza, J. J. Jiménez, P. Revuelta, and M. L. Mora, “Using Bayesian networks in the construction of a bi-level multi–classifier. A case study using intensive care unit patients data”, Artificial Intelligence in Medicine, vol. 22, pp. 233-248, 2001. 


  • González, C., J. A. Lozano, and P. Larrañaga, “Analyzing the population based incremental learning algorithm by means of discrete dynamical systems”, Complex Systems, vol. 12, no. 4, pp. 465–479, 2000. 
  • Inza, I., P. Larrañaga, R. Etxeberria, and B. Sierra, “Feature subset selection by Bayesian network–based optimization”, Artificial Intelligence, vol. 123, pp. 157-184, 2000. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering”, Pattern Recognition Letters, vol. 21, no. 8, pp. 779-786, 2000. 


  • Inza, I., P. Larrañaga, B. Sierra, R. Etxeberria, J. A. Lozano, and J. M. Peña, “Representing the behaviour of supervised classification learning algorithms by Bayesian networks”, Pattern Recognition Letters, vol. 20, no. 11-13, pp. 1201-1209, 1999. 
  • Larrañaga, P., C. M. H. Kuijpers, R. H. Murga, I. Inza, and S. Dizdarevich, “Genetic algorithms for the travelling salesman problem: A review of representations and operators”, Artificial Intelligence Review, vol. 13, pp. 129-170, 1999. 
  • Lozano, J. A., P. Larrañaga, M. Graña, and F. X. Albizuri, “Genetic algorithms: bridging the convergence gap”, Theoretical Computer Science, vol. 229, pp. 11-22, 1999. 
  • Lozano, J. A., and P. Larrañaga, “Applying genetic algorithms to search for the best hierarchical clustering of a dataset”, Pattern Recognition Letters, vol. 20, no. 9, pp. 911-918, 1999. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “An empirical comparison of four initialization methods for the k-means algorithm”, Pattern Recognition Letters, vol. 20, pp. 1027-1040, 1999. 
  • Peña, J. M., J. A. Lozano, and P. Larrañaga, “Learning Bayesian networks for clustering by means of constructive induction”, Pattern Recognition Letters, vol. 20, no. 11-13, pp. 1219-1230, 1999. 


  • Sierra, B., and P. Larrañaga, “Predicting the survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches”, Artificial Intelligence in Medicine, vol. 14, no. 1-2, pp. 215-230, 1998. 


  • Albizuri, X., A. D’Anjou, M. Graña, and P. Larrañaga, “Structure of the high-order Boltzman machine from independence maps”, IEEE Transactions on Neural Networks, vol. 8, no. 6, pp. 1351-1358, 1997. 
  • Etxeberria, R., P. Larrañaga, and J. M. Pikaza, “Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data”, Pattern Recognition Letters, vol. 18, no. 11-13, pp. 1269-1273, 1997. 
  • Larrañaga, P., C. M. H. Kuijpers, M. Poza, and R. H. Murga, “Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms”, Statistics and Computing, vol. 7, no. 1, pp. 19-34, 1997. 


  • Larrañaga, P., M. Poza, Y. Yurramendi, R. H. Murga, and C. M. H. Kuijpers, “Structure learning of Bayesian networks by genetic algorithms: A performance analysis of control parameters”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 9, pp. 912-926, 1996. 
  • Larrañaga, P., C. M. H. Kuijpers, R. H. Murga, and Y. Yurramendi, “Learning Bayesian network structures by searching for the best ordering with genetic algorithms”, IEEE Transactions on System, Man and Cybernetics. Part A: Systems and Humans, vol. 26, no. 4, pp. 487-493, 1996. 


  • Emparanza, J. I., L. Aldámiz-Echevarria, E. G. Pérez-Yarza, P. Larrañaga, J. L. Jimenez, M. Labiano, and I. Ozcoidi, “Prognostic score in acute meningococcemia”, Critical Care Medicine, vol. 16, no. 2, pp. 168-169, 1988.