P. Larrañaga

发表

Bioinformatics Advance Access published August 24, 2007 A review of feature selection techniques in bioinformatics

Pedro Larrañaga, Yvan Saeys, Iñaki Inza, 2022 .

Bayesian Chain Classifiers for Multidimensional Classification

Concha Bielza, Pedro Larrañaga, Luis Enrique Sucar, 2011, IJCAI.

Multi-label classification with Bayesian network-based chain classifiers

Concha Bielza, Pedro Larrañaga, Pablo Hernandez-Leal, 2014, Pattern Recognit. Lett..

Towards a New Evolutionary Computation: Advances on Estimation of Distribution Algorithms (Studies in Fuzziness and Soft Computing)

J. A. Lozano, Iñaki Inza, P. Larrañaga, 2006 .

Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, J. A. Lozano, 2002, Genetic Algorithms and Evolutionary Computation.

An Introduction to Probabilistic Graphical Models

Pedro Larrañaga, P. Larrañaga, 2002, Estimation of Distribution Algorithms.

Classification of neocortical interneurons using affinity propagation

R. Yuste, C. Bielza, P. Larrañaga, 2013, Front. Neural Circuits.

Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms

Pedro Larrañaga, Carlos Cotta, Ramón Sagarna, 2002, Estimation of Distribution Algorithms.

A Review on Estimation of Distribution Algorithms

Pedro Larrañaga, P. Larrañaga, 2002, Estimation of Distribution Algorithms.

Analyzing the PBIL Algorithm by Means of Discrete Dynamical Systems

J. A. Lozano, P. Larrañaga, Cristina González, 2000 .

Benefits of Data Clustering in Multimodal Function Optimization via EDAs

Pedro Larrañaga, Jose A. Lozano, Jose M. Peña, 2002, Estimation of Distribution Algorithms.

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2002, Machine Learning.

Multi-dimensional classification with Bayesian networks

Concha Bielza, Pedro Larrañaga, Guangdi Li, 2011, Int. J. Approx. Reason..

Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

R. Yuste, C. Bielza, P. Larrañaga, 2010, Developmental neurobiology.

An empirical comparison of four initialization methods for the K-Means algorithm

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

On Gaussian Markov models for conditional independence

C. Bielza, P. Larrañaga, I. Sánchez, 2016 .

Parkinson's Disease Subtypes Identified from Cluster Analysis of Motor and Non-motor Symptoms

C. Bielza, P. Larrañaga, K. Chaudhuri, 2017, Front. Aging Neurosci..

Predicting dementia development in Parkinson's disease using Bayesian network classifiers

C. Bielza, P. Larrañaga, E. Bengoetxea, 2013, Psychiatry Research: Neuroimaging.

Algorithms

J. A. Lozano, P. Gács, Dóra Erdős, 1992 .

Optimizing logistic regression coefficients for discrimination and calibration using estimation of distribution algorithms

C. Bielza, P. Larrañaga, L. Ohno-Machado, 2008 .

Machine Learning Approach for the Outcome Prediction of Temporal Lobe Epilepsy Surgery

C. Bielza, P. Larrañaga, J. DeFelipe, 2013, PloS one.

Comparing supervised learning methods for classifying sex, age, context and individual Mudi dogs from barking

C. Bielza, P. Larrañaga, T. Faragó, 2015, Animal Cognition.

Properties of Kikuchi approximations constructed from clique based decompositions

J. A. Lozano, P. Larrañaga, R. Santana, 2022 .

Prioritization of candidate cancer genes—an aid to oncogenomic studies

J. A. Lozano, P. Larrañaga, N. López-Bigas, 2008, Nucleic acids research.

Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks

C. Bielza, P. Larrañaga, Bojan Mihaljević, 2020, bioRxiv.

Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks

C. Bielza, P. Larrañaga, J. DeFelipe, 2020, bioRxiv.

Comparing basal dendrite branches in human and mouse hippocampal CA1 pyramidal neurons with Bayesian networks

C. Bielza, P. Larrañaga, J. DeFelipe, 2020, bioRxiv.

L G ] 1 2 N ov 2 01 8 Markov Property in Generative Classifiers

C. Bielza, P. Larrañaga, Eva Riccomagno, 2018 .

PREDICTING THE EQ-5D FROM THE PARKINSON'S DISEASE QUESTIONNAIRE PDQ-8 USING MULTI-DIMENSIONAL BAYESIAN NETWORK CLASSIFIERS

C. Bielza, P. Larrañaga, P. Martínez-Martín, 2014 .

Discrete Bayesian Network Classifiers : A Survey 1

PEDRO LARRAÑAGA, C. Bielza, P. Larrañaga, 2014 .

Random Positions of Dendritic Spines in Human Cerebral Cortex

R. Yuste, C. Bielza, P. Larrañaga, 2014, The Journal of Neuroscience.

Master s Final Project Machine Learning applied to COVID-19

P. Larrañaga, M. Bielza, Serena Alderisi, 2020 .

Correlate with Citation Impact

Sean L. Hill, C. Bielza, P. Larrañaga, 2016 .

Branching angles of pyramidal cell dendrites follow common geometrical design principles in different cortical areas

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014, Scientific Reports.

Discretization of Expression Quantitative Trait Loci in Association Analysis Between Genotypes and Expression Data

C. Bielza, P. Larrañaga, S. Moral, 2015 .

Learning Interpretable Gene Regulatory Networks via Merging Bayesian Networks

P. Larrañaga, Trabajo Fin de Máster, 2019 .

BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks

C. Bielza, P. Larrañaga, N. Bernaola, 2020, European Workshop on Probabilistic Graphical Models.

Optimization in Continuous Domains by Learning and Simulation of Gaussian Networks

Pedro Larrañaga, Jose A. Lozano, Jose M. Peña, 2000 .

Three-dimensional spatial modeling of spines along dendritic networks in human cortical pyramidal neurons

C. Bielza, P. Larrañaga, J. DeFelipe, 2017, PloS one.

Supervised Classification with Gaussian Networks . Filter and Wrapper Approaches

Iñaki Inza, P. Larrañaga, Aritz Pérez, 2004 .

Chapter 2 Machine Learning : An Indispensable Tool in Bioinformatics

J. A. Lozano, Iñaki Inza, P. Larrañaga, 2017 .

Methods’ details for the bnclassify package

C. Bielza, P. Larrañaga, Bojan Mihaljević, 2018 .

New insights into the classification and nomenclature of cortical GABAergic interneurons

Concha Bielza, Pedro Larrañaga, Rafael Yuste, 2013, Nature Reviews Neuroscience.

Novel Tools and Methods Patterns of Dendritic Basal Field Orientation of Pyramidal Neurons in the Rat Somatosensory Cortex

P. Larrañaga, J. DeFelipe, R. Benavides-Piccione, 2019 .

Patterns of Dendritic Basal Field Orientation of Pyramidal Neurons in the Rat Somatosensory Cortex

C. Bielza, P. Larrañaga, J. DeFelipe, 2018, eNeuro.

Wiring Economy of Pyramidal Cells in the Juvenile Rat Somatosensory Cortex

C. Bielza, P. Larrañaga, J. DeFelipe, 2016, PLoS ONE.

Dendritic-branching angles of pyramidal neurons of the human cerebral cortex

C. Bielza, P. Larrañaga, J. DeFelipe, 2016, Brain Structure and Function.

Aalborg Universitet Conditional Density Approximations with Mixtures of Polynomials

Thomas D. Nielsen, Pedro L. López-Cruz, P. Larrañaga, 2014 .

Identifying Parkinson’s disease subtypes with motor and non-motor symptoms via model-based multi-partition clustering

P. Larrañaga, K. Chaudhuri, P. Martínez-Martín, 2021, Scientific Reports.

A review of Gaussian Markov models for conditional independence

Concha Bielza, Pedro Larrañaga, Irene Córdoba, 2019 .

Explainable Machine Learning for Longitudinal Multi-Omic Microbiome

C. Bielza, P. Larrañaga, P. Laccourreye, 2022, Mathematics.

Hybrid semiparametric Bayesian networks

C. Bielza, P. Larrañaga, D. Atienza, 2022, TEST.

Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, Int. J. Approx. Reason..

GECCO ’14: Proceedings of the 2014 conference on Genetic and evolutionary computation

Julian Togelius, Christine Zarges, Hitoshi Iba, 2014 .

Identification of a biomarker panel for colorectal cancer diagnosis

Pedro Larrañaga, Iñaki Inza, Rubén Armañanzas, 2012, BMC Cancer.

Machine Learning : Editorial

Pedro Larrañaga, Jose A. Lozano, Iñaki Inza, 2005 .

Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms

Concha Bielza, Pedro Larrañaga, Yvan Saeys, 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models

Pedro Larrañaga, Iñaki Inza, Jose L. Flores, 2005, Data Analysis and Visualization in Genomics and Proteomics.

Editorial

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2005, Machine Learning.

A new measure for gene expression biclustering based on non-parametric correlation

Pedro Larrañaga, Iñaki Inza, Borja Calvo, 2013, Comput. Methods Programs Biomed..

Floating Search Methods in Learning Bayesian Networks

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2002, Probabilistic Graphical Models.

Bayesian Classifiers with Consensus Gene Selection: A Case Study in the Systemic Lupus Erythematosus

Pedro Larrañaga, Iñaki Inza, Borja Calvo, 2008 .

Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures

Pedro Larrañaga, Iñaki Inza, Borja Calvo, 2009, IEEE Transactions on Information Technology in Biomedicine.

PyBNesian: An extensible python package for Bayesian networks

C. Bielza, P. Larrañaga, D. Atienza, 2022, Neurocomputing.

DISCOVER WILEY'S LEADING STATISTICS JOURNALS

Concha Bielza, Pedro Larrañaga, Serge Rudaz, 2014 .

Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks

C. Bielza, P. Larrañaga, Pedro Fontán, 2022, Int. J. Intell. Syst..

International Journal of Approximate Reasoning Tractability of most probable explanations in multidimensional Bayesian network classiﬁers ✩

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2022 .

Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks

Concha Bielza, Pedro Larrañaga, David Quesada, 2021, Eng. Appl. Artif. Intell..

Affinity propagation enhanced by estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, GECCO '11.

Bayesian Classifier (Naïve Bayes)

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Discriminative Learning of Bayesian Network Classifiers

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2006, Inteligencia Artif..

Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding

Concha Bielza, Pedro Larrañaga, David Quesada, 2021, HAIS.

Cluster methods for assessing research performance: exploring Spanish computer science

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2013, Scientometrics.

Univariate and bivariate truncated von Mises distributions

Concha Bielza, Pedro Larrañaga, Pablo Fernandez-Gonzalez, 2017, Progress in Artificial Intelligence.

Gaussian-Stacking Multiclassifiers for Human Embryo Selection

Pedro Larrañaga, Endika Bengoetxea, P. Larrañaga, 2009 .

Hybrid Gaussian and von Mises Model-Based Clustering

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2016, ECAI.

Regularized k-order markov models in EDAs

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, GECCO '11.

Data-Driven Computational Neuroscience

Concha Bielza, Pedro Larrañaga, C. Bielza, 2020 .

Bayesian networks for interpretable machine learning and optimization

Concha Bielza, Pedro Larrañaga, Bojan Mihaljevic, 2021, Neurocomputing.

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2011, Neuroinformatics.

Bayesian network modeling of the consensus between experts: An application to neuron classification

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014 .

Moral Graph, Triangulation of

Pedro Larrañaga, C.M.H. Kuipers, P. Larrañaga, 2006 .

GUEST EDITORIAL: Data mining in genomics and proteomics

Pedro Larrañaga, Víctor Robles, Ernestina Menasalvas, 2004 .

An Online Feature Selection Methodology for Ball-Bearing Harmonic Frequencies Based on HMMs

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2021, SOCO.

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data

Concha Bielza, Pedro Larrañaga, Miguel García-Torres, 2013, Inf. Sci..

Finite Mixture Model

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Circular Bayesian classifiers using wrapped Cauchy distributions

Concha Bielza, Pedro Larrañaga, Ignacio Leguey, 2019, Data Knowl. Eng..

A univocal definition of the neuronal soma morphology using Gaussian mixture models

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2015, Front. Neuroanat..

Machine-Level Case Study: Fingerprint of Industrial Motors

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

Applications of Machine Learning in Industrial Sectors

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2019, IEEE Access.

Component-Level Case Study: Remaining Useful Life of Bearings

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

Production-Level Case Study: Automated Visual Inspection of a Laser Process

Concha Bielza, Pedro Larrañaga, David Atienza, 2018, Industrial Applications of Machine Learning.

Estimation of Distribution Algorithm (EDA)

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Stratified Cross-Validation in Multi-Label Classification Using Genetic Algorithms

Concha Bielza, Pedro Larrañaga, C. Bielza, 2013 .

Affinity Propagation-based Clustering

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Laminar Differences in Dendritic Structure of Pyramidal Neurons in the Juvenile Rat Somatosensory Cortex

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2016, Cerebral cortex.

Ensemble of Classifiers

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

The Convergence Behavior of the PBIL Algorithm

Pedro Larrañaga, Jose A. Lozano, Cristina González, 2001 .

Genetic algorithms applied to Bayesian networks

Pedro Larrañaga, Jose A. Lozano, Manuel Graña, 1996 .

Independent Component Analysis (ICA)

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Validation Measures for Clustering

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Estimation of distribution algorithms with affinity propagation methods

Pedro Larrañaga, Roberto Santana, Jose Antonio Lozano, 2008 .

Learning a L 1-regularized Gaussian Bayesian network in the space of equivalence classes

C. Bielza, P. Larrañaga, D. Vidaurre, 2008 .

Asymmetric HMMs for Online Ball-Bearing Health Assessments

C. Bielza, P. Larrañaga, J. Labarta, 2022, IEEE Internet of Things Journal.

Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling

C. Bielza, P. Larrañaga, Enrique Valero-Leal, 2022, PGM.

Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection

C. Bielza, P. Larrañaga, Jorge Casajús-Setién, 2022, PGM.

Structure learning algorithms for multidimensional continuous-time Bayesian network classifiers

C. Bielza, P. Larrañaga, Fabio Stella, 2022, PGM.

Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables

Vicente P. Soloviev, C. Bielza, P. Larrañaga, 2022, J. Comb. Optim..

Feature Saliencies in Asymmetric Hidden Markov Models.

C. Bielza, P. Larrañaga, Carlos Puerto-Santana, 2022, IEEE transactions on neural networks and learning systems.

Lazy lasso for local regression

C. Bielza, P. Larrañaga, D. Vidaurre, 2011, Computational Statistics.

A review on probabilistic graphical models in evolutionary computation

C. Bielza, P. Larrañaga, Roberto Santana, 2012, Journal of Heuristics.

Dendritic-branching angles of pyramidal neurons of the human cerebral cortex

C. Bielza, P. Larrañaga, J. DeFelipe, 2016, Brain Structure and Function.

Interval-based ranking in noisy evolutionary multi-objective optimization

C. Bielza, P. Larrañaga, Hossein Karshenas, 2014, Computational Optimization and Applications.

Network design through forests with degree- and role-constrained minimum spanning trees

C. Bielza, P. Larrañaga, Laura Anton-Sanchez, 2017, Journal of Heuristics.

Cluster methods for assessing research performance: exploring Spanish computer science

C. Bielza, P. Larrañaga, Alfonso Ibáñez, 2013, Scientometrics.

Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2010, Evolutionary Computation.

Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms

Pedro Larrañaga, Jose A. Lozano, Iñaki Inza, 2006, Towards a New Evolutionary Computation.

Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

Pedro Larrañaga, Yosu Yurramendi, Cindy M. H. Kuijpers, 1996, IEEE Trans. Pattern Anal. Mach. Intell..

Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms

Pedro Larrañaga, Cindy M. H. Kuijpers, Mikel Poza, 1995, AISTATS.

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2016, J. Assoc. Inf. Sci. Technol..

A community-based transcriptomics classification and nomenclature of neocortical cell types

Concha Bielza, Moritz Helmstaedter, Hongkui Zeng, 2019, Nature Neuroscience.

Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, Neuroinformatics.

Continuous Estimation of Distribution Algorithms Based on Factorized Gaussian Markov Networks

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012 .

A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks

Concha Bielza, Pedro Larrañaga, Shogo Kato, 2019, Inf. Sci..

Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions

Concha Bielza, Pedro Larrañaga, Ignacio Leguey, 2016, CAEPIA.

Directional naive Bayes classifiers

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2015, Pattern Analysis and Applications.

Dendritic branching angles of pyramidal cells across layers of the juvenile rat somatosensory cortex

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2016, The Journal of comparative neurology.

Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering

Pedro Larrañaga, José Antonio Lozano, Guzmán Santafé, 2008, J. Comput. Biol..

Learning Bayesian networks for clustering by means of constructive induction

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 1999, Pattern Recognit. Lett..

Quantum approximate optimization algorithm for Bayesian network structure learning

Vicente P. Soloviev, C. Bielza, P. Larrañaga, 2022, Quantum Information Processing.

A Survey of L1 Regression

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013 .

Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2005, J. Biomed. Informatics.

A review of estimation of distribution algorithms in bioinformatics

Concha Bielza, Pedro Larrañaga, Yvan Saeys, 2008, BioData Mining.

Adaptive Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, Adaptive and Multilevel Metaheuristics.

A survey on multi‐output regression

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2015, WIREs Data Mining Knowl. Discov..

Data Mining Validation of Fluconazole Breakpoints Established by the European Committee on Antimicrobial Susceptibility Testing

C. Bielza, P. Larrañaga, M. Cuenca‐Estrella, 2009, Antimicrobial Agents and Chemotherapy.

Performance evaluation of compromise conditional Gaussian networks for data clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, Int. J. Approx. Reason..

An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2000, Pattern Recognit. Lett..

Partitional Cluster Analysis with Genetic Algorithms: Searching for the Number of Clusters

Pedro Larrañaga, Manuel Graña, Jose A. Lozano, 1998 .

Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering

Pedro Larrañaga, Manuel Graña, Jose A. Lozano, 1997, ICANNGA.

Evolutionary strategies for adaptive color quantization

Pedro Larrañaga, José Antonio Lozano Alonso, Manuel Graña Romay, 1996 .

Mouse p53-Deficient Cancer Models as Platforms for Obtaining Genomic Predictors of Human Cancer Clinical Outcomes

C. Bielza, P. Larrañaga, R. Rosell, 2012, PloS one.

Feature subset selection from positive and unlabelled examples

Pedro Larrañaga, José Antonio Lozano, Borja Calvo, 2009, Pattern Recognit. Lett..

Akaike Information Criterion

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014 .

Semi-supervised projected model-based clustering

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2014, Data Mining and Knowledge Discovery.

Classifying evolving data streams with partially labeled data

Concha Bielza, Pedro Larrañaga, Hanen Borchani, 2011, Intell. Data Anal..

Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances

Concha Bielza, Pedro Larrañaga, Hanen Borchani, 2010, IEA/AIE.

A review of Gaussian Markov models for conditional independence

C. Bielza, P. Larrañaga, Irene Córdoba, 2019 .

Bayesian Sparse Partial Least Squares

Concha Bielza, Pedro Larrañaga, Tom Heskes, 2013, Neural Computation.

Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators

Pedro Larrañaga, Iñaki Inza, Cindy M. H. Kuijpers, 1999, Artificial Intelligence Review.

Context-specific kernel-based hidden Markov model for time series analysis

C. Bielza, P. Larrañaga, G. Henter, 2023, 2301.09870.

Learning Bayesisan Networks by Genetic Algorithms: A Case Study in the Prediction of Survival in Malignant Skin Melanoma

Pedro Larrañaga, Basilio Sierra, Juan Manuel Pikatza Atxa, 1997, AIME.

Machine learning: an indispensable tool in bioinformatics.

Pedro Larrañaga, Iñaki Inza, Endika Bengoetxea, 2010, Methods in molecular biology.

Machine Learning in Bioinformatics

Concha Bielza, Pedro Larrañaga, Iñaki Inza, 2008, Encyclopedia of Database Systems.

Gene Selection For Cancer Classification Using Wrapper Approaches

ROSA BLANCO, PEDRO LARRAÑAGA, IÑAKI INZA, 2004, Int. J. Pattern Recognit. Artif. Intell..

Filter versus wrapper gene selection approaches in DNA microarray domains

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2004, Artif. Intell. Medicine.

Sparse regularized local regression

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013, Comput. Stat. Data Anal..

Lazy lasso for local regression

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2012, Comput. Stat..

On nonlinearity in neural encoding models applied to the primary visual cortex

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2011, Network.

A partially supervised classification approach to dominant and recessive human disease gene prediction

Pedro Larrañaga, Borja Calvo, Núria López-Bigas, 2007, Comput. Methods Programs Biomed..

3D morphology-based clustering and simulation of human pyramidal cell dendritic spines

Concha Bielza, Pedro Larrañaga, Sergio Luengo-Sanchez, 2018, PLoS Comput. Biol..

Feature Subset Selection by Bayesian network-based optimization

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2000, Artif. Intell..

EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization

Pedro Larrañaga, Endika Bengoetxea, P. Larrañaga, 2010, ANTS Conference.

Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains

Pedro Larrañaga, Endika Bengoetxea, Alexander Mendiburu, 2007, Connect. Sci..

Experimental Results in Function Optimization with EDAs in Continuous Domain

Pedro Larrañaga, Jose A. Lozano, Endika Bengoetxea, 2002, Estimation of Distribution Algorithms.

An Empirical Comparison Between K-Means, GAs and EDAs in Partitional Clustering

Pedro Larrañaga, Josep Roure Alcobé, Ramon Sangüesa, 2002, Estimation of Distribution Algorithms.

Estimation of Distribution Algorithms as Logistic Regression Regularizers of Microarray Classifiers

Concha Bielza, P Larranaga, C. Bielza, 2009, Methods of Information in Medicine.

Three-dimensional distribution of cortical synapses: a replicated point pattern-based analysis

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014, Front. Neuroanat..

Three-Dimensional Spatial Distribution of Synapses in the Neocortex: A Dual-Beam Electron Microscopy Study

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2013, Cerebral cortex.

Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes

Pedro Larrañaga, Iñaki Inza, Aritz Pérez Martínez, 2006, Int. J. Approx. Reason..

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm

María S. Pérez-Hernández, Pedro Larrañaga, José M. Peña, 2006, Towards a New Evolutionary Computation.

GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms

María S. Pérez-Hernández, Pedro Larrañaga, Francisco Rosales, 2004, IEA/AIE.

Evolutionary computation based on Bayesian classifiers

Pedro Larrañaga, Endika Bengoetxea, Teresa Miquélez, 2004 .

Feature subset selection by genetic algorithms and estimation of distribution algorithms - A case study in the survival of cirrhotic patients treated with TIPS

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, Artif. Intell. Medicine.

Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks

Pedro Larrañaga, Iñaki Inza, José Antonio Lozano, 2001, IEEE Trans. Pattern Anal. Mach. Intell..

Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2016, Neuroinformatics.

Representing the behaviour of supervised classification learning algorithms by Bayesian networks

I Inza, P Larrañaga, B Sierra, 1999, Pattern Recognit. Lett..

Conditional Density Approximations with Mixtures of Polynomials

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2015, Int. J. Intell. Syst..

Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2014, Int. J. Approx. Reason..

Learning mixtures of polynomials from data using B-spline interpolation

Concha Bielza, Pedro Larrañaga, Pedro L. López-Cruz, 2012 .

Classification of neural signals from sparse autoregressive features

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013, Neurocomputing.

The Vallecas Project: A Cohort to Identify Early Markers and Mechanisms of Alzheimer’s Disease

C. Bielza, P. Larrañaga, P. Martínez-Martín, 2015, Front. Aging Neurosci..

Bayesian classifiers based on kernel density estimation: Flexible classifiers

Pedro Larrañaga, Iñaki Inza, Aritz Pérez Martínez, 2009, Int. J. Approx. Reason..

Applying genetic algorithms to search for the best hierarchical clustering of a dataset

Pedro Larrañaga, José Antonio Lozano, J. A. Lozano, 1999, Pattern Recognit. Lett..

Tree-structured Bayesian networks for wrapped

C. Bielza, P. Larrañaga, 2016 .

Quantum parametric circuit optimization with estimation of distribution algorithms

Vicente P. Soloviev, C. Bielza, P. Larrañaga, 2022, GECCO Companion.

Bayesian Networks, Rule Induction and Logistic Regression in the Prediction of the Survival of Women Suffering from Breast Cancer

Pedro Larrañaga, Basilio Sierra, Miren J. Gallego, 1997, EPIA.

The Fourth Industrial Revolution

C. Bielza, P. Larrañaga, P. Bianchi, 2016, Teaching and Learning in the 21st Century.

Regularized continuous estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2013, Appl. Soft Comput..

Bayesian Information Criterion (BIC)

C. Bielza, P. Larrañaga, 2014 .

bnclassify: Learning Bayesian Network Classifiers

Concha Bielza, Pedro Larrañaga, Bojan Mihaljević, 2018, R J..

Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms

Pedro Larrañaga, Cindy M. H. Kuijpers, Mikel Poza, 1997, Stat. Comput..

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables

Concha Bielza, Pedro Larrañaga, Juan A. Fernández del Pozo, 2013, Journal of Computer Science and Technology.

Genetic Algorithms: Bridging the Convergence Gap

Pedro Larrañaga, Manuel Graña, José Antonio Lozano, 1999, Theor. Comput. Sci..

Learning Bayesian network structures by searching for the best ordering with genetic algorithms

Pedro Larrañaga, Yosu Yurramendi, Cindy M. H. Kuijpers, 1996, IEEE Trans. Syst. Man Cybern. Part A.

Finite Mixture Model

C. Bielza, P. Larrañaga, 2014 .

Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 2000–2009

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2013, Scientometrics.

Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables

Concha Bielza, Pedro Larrañaga, Endika Bengoetxea, 2010, Expert Syst. Appl..

Interval-based ranking in noisy evolutionary multi-objective optimization

Concha Bielza, Pedro Larrañaga, Hossein Karshenas, 2015, Comput. Optim. Appl..

A review of undirected and acyclic directed Gaussian Markov model selection and estimation pdf

Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga, 2016, ArXiv.

Probabilistic graphical models in artificial intelligence

Pedro Larrañaga, Serafín Moral, P. Larrañaga, 2011, Appl. Soft Comput..

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

Concha Bielza, Pedro Larrañaga, José Antonio Lozano Alonso, 2010 .

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers

Pedro Larrañaga, Iñaki Inza, Rubén Armañanzas, 2008, Comput. Methods Programs Biomed..

A review on evolutionary algorithms in Bayesian network learning and inference tasks

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2013, Inf. Sci..

Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches

Pedro Larrañaga, Basilio Sierra, P. Larrañaga, 1998, Artif. Intell. Medicine.

Interactions and dependencies in estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2005, 2005 IEEE Congress on Evolutionary Computation.

Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach

Concha Bielza, Pedro Larrañaga, Kallol Ray Chaudhuri, 2013, Artif. Intell. Medicine.

Predicting the h-index with cost-sensitive naive Bayes

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2011, 2011 11th International Conference on Intelligent Systems Design and Applications.

Wrapper discretization by means of estimation of distribution algorithms

Pedro Larrañaga, Iñaki Inza, Jose Luis Flores, 2007, Intell. Data Anal..

Multidimensional continuous time Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, Carlos Villa-Blanco, 2021, Int. J. Intell. Syst..

Industrial Applications of Machine Learning

Concha Bielza, Pedro Larrañaga, David Atienza, 2018 .

Research topics in discrete estimation of distribution algorithms based on factorizations

Pedro Larrañaga, Jose A. Lozano, Roberto Santana, 2009, Memetic Comput..

Triangulation of Bayesian networks with recursive estimation of distribution algorithms

Pedro Larrañaga, Txomin Romero, P. Larrañaga, 2009, Int. J. Approx. Reason..

Estimation of Distribution Algorithms Applied To Combinatorial Optimization Problems

Pedro Larrañaga, Jose A. Lozano, H. Mühlenbein, 2003 .

Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems

Isabelle Bloch, Pedro Larrañaga, Endika Bengoetxea, 2001, EMMCVPR.

Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, AIME.

Network design through forests with degree- and role-constrained minimum spanning trees

Concha Bielza, Pedro Larrañaga, Laura Anton-Sanchez, 2017, J. Heuristics.

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2014, IEEE Transactions on Evolutionary Computation.

Towards optimal neuronal wiring through estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Laura Anton-Sanchez, 2013, GECCO.

A review on probabilistic graphical models in evolutionary computation

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012, J. Heuristics.

Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms

Concha Bielza, Pedro Larrañaga, Endika Bengoetxea, 2011, J. Heuristics.

Regularized logistic regression without a penalty term: An application to cancer classification with microarray data

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2011, Expert Syst. Appl..

Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2011, EMO.

Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms

José Ignacio Hidalgo, Concha Bielza, Pedro Larrañaga, 2010, IEEE Congress on Evolutionary Computation.

Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, PPSN.

Protein Folding in Simplified Models With Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2008, IEEE Transactions on Evolutionary Computation.

Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, J. Heuristics.

Exact Bayesian network learning in estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, 2007 IEEE Congress on Evolutionary Computation.

The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, EvoBIO.

Optimization by Max-Propagation Using Kikuchi Approximations

Pedro Larrañaga, Robin Höns, Jose A. Lozano, 2007 .

Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains

Pedro Larrañaga, Endika Bengoetxea, Teresa Miquélez, 2006, SEAL.

Towards a supervised classification of neocortical interneuron morphologies

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2018, BMC Bioinformatics.

Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2015, Neuroinformatics.

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2014, Front. Comput. Neurosci..

Multi-dimensional Bayesian network classifiers: A survey

Concha Bielza, Santiago Gil-Begue, Pedro Larrañaga, 2020, Artificial Intelligence Review.

Analyzing the Population Based Incremental Learning Algorithm by Means of Discrete Dynamical Systems

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2000, Complex Syst..

Bayesian networks in neuroscience: a survey

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014, Front. Comput. Neurosci..

Network measures for information extraction in evolutionary algorithms

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2013, Int. J. Comput. Intell. Syst..

Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2010, IEA/AIE.

Mining probabilistic models learned by EDAs in the optimization of multi-objective problems

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2009, GECCO.

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)

C. Bielza, P. Larrañaga, M. Cuenca‐Estrella, 2010, Antimicrobial Agents and Chemotherapy.

Learning tractable Bayesian networks in the space of elimination orders

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2019, Artif. Intell..

Learning Bayesian networks with low inference complexity

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2016, Progress in Artificial Intelligence.

Maximizing the number of polychronous groups in spiking networks

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012, GECCO '12.

Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2018, IEEE Internet of Things Journal.

Autoregressive Asymmetric Linear Gaussian Hidden Markov Models

C. Bielza, P. Larrañaga, Carlos Puerto-Santana, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Asymmetric Hidden Markov Models with Continuous Variables

Concha Bielza, Pedro Larrañaga, Carlos Puerto-Santana, 2018, CAEPIA.

Discrete Bayesian Network Classifiers

Concha Bielza, Pedro Larrañaga, C. Bielza, 2014, ACM Comput. Surv..

Learning Bayesian networks from data . Some applications in biomedicine

Pedro Larrañaga, P. Larrañaga, 2002 .

On generating random Gaussian graphical models

C. Bielza, P. Larrañaga, Gherardo Varando, 2019, Int. J. Approx. Reason..

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices

Concha Bielza, Pedro Larrañaga, Irene Córdoba, 2018, IDEAL.

Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, João Gama, 2016, Intell. Data Anal..

BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience

Concha Bielza, Pedro Larrañaga, Mario Michiels, 2020, bioRxiv.

Tractable learning of Bayesian networks from partially observed data

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2019, Pattern Recognit..

Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs

Luis M. de Campos, Pedro Larrañaga, Serafín Moral, 2002, Estimation of Distribution Algorithms.

Predicting citation count of Bioinformatics papers within four years of publication

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2009, Bioinform..

MultiMap: A Tool to Automatically Extract and Analyse Spatial Microscopic Data From Large Stacks of Confocal Microscopy Images

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2018, Front. Neuroanat..

Efficient search for relevance explanations using MAP-independence in Bayesian networks

C. Bielza, P. Larrañaga, S. Renooij, 2023, International Journal of Approximate Reasoning.

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks — A Case Study for the Optimal Ordering of Tables

C. Bielza, P. Larrañaga, J. A. F. Pozo, 2013, Journal of Computer Science and Technology.

Machine Learning-based CPS for Clustering High throughput Machining Cycle Conditions

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2017 .

Probabilistic Graphical Markov Model Learning: An Adaptive Strategy

Concha Bielza, Pedro Larrañaga, Eunice Ponce de León, 2009, MICAI.

Inexact graph matching by means of estimation of distribution algorithms

Isabelle Bloch, Pedro Larrañaga, Endika Bengoetxea, 2002, Pattern Recognit..

Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions

Pedro Larrañaga, José Antonio Lozano, Cristina González, 2002, Int. J. Approx. Reason..

Combinatonal Optimization by Learning and Simulation of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, Ramon Etxeberria, 2000, UAI.

Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2016, ML4CPS.

Patient specific prediction of temporal lobe epilepsy surgical outcomes

Jeffery A. Hall, C. Bielza, P. Larrañaga, 2021, Epilepsia.

Learning Tractable Multidimensional Bayesian Network Classifiers

Concha Bielza, Pedro Larrañaga, Marco Benjumeda, 2016, Probabilistic Graphical Models.

Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian Networks

Concha Bielza, Pedro Larrañaga, Mario Michiels, 2020, bioRxiv.

Ensemble transcript interaction networks: A case study on Alzheimer's disease

Concha Bielza, Pedro Larrañaga, Rubén Armañanzas, 2012, Comput. Methods Programs Biomed..

Medical Bayes Networks

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2000, ISMDA.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2015, Artif. Intell. Medicine.

Semi-supervised Projected Clustering for Classifying GABAergic Interneurons

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2013, AIME.

Data Publications Correlate with Citation Impact

Concha Bielza, Pedro Larrañaga, Florian Leitner, 2016, Front. Neurosci..

Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data

Pedro Larrañaga, Ramon Etxeberria, Juan Manuel Pikatza Atxa, 1997, Pattern Recognit. Lett..

Side chain placement using estimation of distribution algorithms

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2007, Artif. Intell. Medicine.

Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers

C. Bielza, P. Larrañaga, Fabio Stella, 2023, Int. J. Approx. Reason..

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2001, Artif. Intell. Medicine.

Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering

Concha Bielza, Pedro Larrañaga, Javier Diaz-Rozo, 2020, Eng. Appl. Artif. Intell..

Incremental Learning of Latent Forests

Concha Bielza, Pedro Larrañaga, Fernando Rodriguez-Sanchez, 2020, IEEE Access.

Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 1999, AIMDM.

Feature Subset Selection by Estimation of Distribution Algorithms

Pedro Larrañaga, Iñaki Inza, Basilio Sierra, 2002, Estimation of Distribution Algorithms.

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2005, Evolutionary Computation.

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)

Concha Bielza, Pablo Martinez-Martin, Pedro Larranaga, 2012, J. Biomed. Informatics.

Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2014, Neurocomputing.

A community-based transcriptomics classification and nomenclature of neocortical cell types

Eric S. Kuebler, Homeira Moradi Chameh, R. Yuste, 2020, Nature Neuroscience.

Multipartition clustering of mixed data with Bayesian networks

C. Bielza, P. Larrañaga, Fernando Rodriguez-Sanchez, 2021, Int. J. Intell. Syst..

Observational Study of the Efficiency of Treatments in Patients Hospitalized with Covid-19 in Madrid

C. Bielza, P. Larrañaga, N. Bernaola, 2020, medRxiv.

Author Correction: A community-based transcriptomics classification and nomenclature of neocortical cell types

Eric S. Kuebler, Homeira Moradi Chameh, R. Yuste, 2021, Nature neuroscience.

Regularized logistic regression and multiobjective variable selection for classifying MEG data

Concha Bielza, Pedro Larrañaga, Roberto Santana, 2012, Biological Cybernetics.

Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem

Concha Bielza, Pedro Larrañaga, Vicente P. Soloviev, 2021, 2021 IEEE Congress on Evolutionary Computation (CEC).

Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons

C. Bielza, P. Larrañaga, J. DeFelipe, 2014, Neuroinformatics.

Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals

Concha Bielza, Pedro Larrañaga, Alfonso Ibáñez, 2011, Scientometrics.

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, Hanen Borchani, 2013, Artif. Intell. Medicine.

Multi-Dimensional Classification with Super-Classes

C. Bielza, P. Larrañaga, J. Read, 2014, IEEE Trans. Knowl. Data Eng..

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2014, Probabilistic Graphical Models.

Decision functions for chain classifiers based on Bayesian networks for multi-label classification

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2016, Int. J. Approx. Reason..

Decision boundary for discrete Bayesian network classifiers

Concha Bielza, Pedro Larrañaga, Gherardo Varando, 2015, J. Mach. Learn. Res..

Selection of human embryos for transfer by Bayesian classifiers

Pedro Larrañaga, Endika Bengoetxea, Dinora Araceli Morales, 2008, Comput. Biol. Medicine.

Learning Bayesian classifiers from positive and unlabeled examples

Pedro Larrañaga, José Antonio Lozano, Borja Calvo, 2007, Pattern Recognit. Lett..

Estimation of Distribution Algorithms Applied To Combinatorial Optimization Problems

J. A. Lozano, H. Mühlenbein, P. Larrañaga, 2003 .

Causal reinforcement learning based on Bayesian networks applied to industrial settings

C. Bielza, P. Larrañaga, David Quesada, 2023, Eng. Appl. Artif. Intell..

A comparison of clustering quality indices using outliers and noise

Concha Bielza, Pedro Larrañaga, Víctor Robles, 2012, Intell. Data Anal..

Publisher Correction: A community-based transcriptomics classification and nomenclature of neocortical cell types

Eric S. Kuebler, R. Yuste, P. Somogyi, 2020, Nature Neuroscience.

Classification of GABAergic interneurons by leading neuroscientists

Concha Bielza, Pedro Larrañaga, Javier DeFelipe, 2019, Scientific Data.

Towards Gaussian Bayesian Network Fusion

Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga, 2015, ECSQARU.

Supplementary Material Three-Dimensional Spatial Distribution of Synapses in the Neocortex : a Dual-Beam Electron Microscopy Study

C. Bielza, P. Larrañaga, J. DeFelipe, 2012 .

Mixtures of Kikuchi Approximations

Pedro Larrañaga, Roberto Santana, José Antonio Lozano, 2006, ECML.

Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks

Irene Córdoba-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, 2018, CAEPIA.

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data

Pedro Larrañaga, Endika Bengoetxea, Miguel García, 2008, Comput. Methods Programs Biomed..

Gene expression A review of feature selection techniques in bioinformatics

Y. Saeys, Iñaki Inza, P. Larrañaga, 2007 .

Multi-label classification with Bayesian network-based chain classifiers

Julio H. Zaragoza, E. Morales, C. Bielza, 2014, Pattern Recognition Letters.

Learning Bayesian networks in the space of structures by estimation of distribution algorithms

Pedro Larrañaga, Iñaki Inza, Rosa Blanco, 2003, Int. J. Intell. Syst..

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

José Antonio Lozano Alonso, Roberto Santana Hermida, C. Bielza, 2010 .

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks

Pedro Larrañaga, José Antonio Lozano, José M. Peña, 2001, AISTATS.

Identification of a biomarker panel for colorectal cancer diagnosis

Iñaki Inza, P. Larrañaga, B. Suárez-Merino, 2012, BMC Cancer.

Learning Bayesian Networks by Floating Search Methods

Pedro Larrañaga, Rosa Blanco, Iiiaki Inza, 2004 .

What is behind a summary-evaluation decision?

Pedro Larrañaga, Ana Arruarte, Iraide Zipitria, 2008, Behavior research methods.

Feature subset selection for data and feature streams: a review

C. Bielza, P. Larrañaga, Carlos Villa-Blanco, 2023, Artificial Intelligence Review.

Efficient Anomaly Detection in a Laser-Surface Heat-Treatment Process via Laser-Spot Tracking

C. Bielza, P. Larrañaga, D. Atienza, 2021, IEEE/ASME Transactions on Mechatronics.

Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot

Concha Bielza, Pedro Larrañaga, David Atienza, 2016, STAIRS.

Estimation of Distribution Algorithms

J. A. Lozano, P. Larrañaga, P. Posík, 2002, Genetic Algorithms and Evolutionary Computation.

Genetic Algorithms Elitist Probabilistic of Degree 1, a generalization of Simulated Annealing

Pedro Larrañaga, Manuel Graña, Alicia D'Anjou, 1993, AI*IA.

ong-term forecasting of multivariate time series in industrial furnaces with ynamic Gaussian Bayesian networks

C. Bielza, P. Larrañaga, G. Valverde, 2021 .

Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters

Cindy M. H. Kuijpers, P. Larrañaga, R. Murga, 1996, IEEE Trans. Pattern Anal. Mach. Intell..

Rejoinder on: Hybrid semiparametric Bayesian networks

C. Bielza, P. Larrañaga, D. Atienza, 2022, TEST.

Semiparametric Bayesian Networks

Concha Bielza, Pedro Larranaga, David Atienza, 2021, Inf. Sci..

An L1-Regularized naïVE Bayes-Inspired Classifier for Discarding Redundant and Irrelevant Predictors

Concha Bielza, Pedro Larrañaga, Diego Vidaurre, 2013, Int. J. Artif. Intell. Tools.

Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space

C. Bielza, P. Larrañaga, D. Vidaurre, 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Regularization (Ridge, Lasso, Elastic Net, Fused Lasso, Group Lasso)

C. Bielza, P. Larrañaga, 2014 .

Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data

C. Bielza, P. Larrañaga, Gherardo Varando, 2020, IEEE Access.

The Convergence Behavior of the PBIL Algorithm: A Preliminary Approach

Pedro Larrañaga, Jose A. Lozano, J. A. Lozano, 2001 .

Bayesian Chain Classifiers for Multidimensional Classification

Julio H. Zaragoza, C. Bielza, L. Sucar, 2011, IJCAI.

Frobenius Norm Regularization for the Multivariate Von Mises Distribution

Concha Bielza, Pedro Larrañaga, Luis Rodriguez-Lujan, 2017, Int. J. Intell. Syst..

Variational Quantum Algorithm Parameter Tuning with Estimation of Distribution Algorithms

Vicente P. Soloviev, C. Bielza, P. Larrañaga, 2023, IEEE Congress on Evolutionary Computation.

Semiparametric Estimation of Distribution Algorithms for Continuous Optimization

Vicente P. Soloviev, C. Bielza, P. Larrañaga, 2023, IEEE Transactions on Evolutionary Computation.

Regularized Multivariate von Mises Distribution

Concha Bielza, Pedro Larrañaga, Luis Rodriguez-Lujan, 2015, CAEPIA.

Solving Graph Matching with EDAs Using a Permutation-Based Representation

Isabelle Bloch, Pedro Larrañaga, Endika Bengoetxea, 2002, Estimation of Distribution Algorithms.

A regularity index for dendrites - local statistics of a neuron's input space

Concha Bielza, Pedro Larrañaga, Laura Anton-Sanchez, 2018, PLoS Comput. Biol..

Tractability of most probable explanations in multidimensional Bayesian network classifiers

C. Bielza, P. Larrañaga, Marco Benjumeda, 2018, Int. J. Approx. Reason..