Pedro Larrañaga

According to our database1, Pedro Larrañaga authored at least 238 papers between 1993 and 2021.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2021
BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience.
Neurocomputing, 2021

Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks.
Frontiers Neuroinformatics, 2021

Multi-dimensional Bayesian network classifiers: A survey.
Artif. Intell. Rev., 2021

2020
On generating random Gaussian graphical models.
Int. J. Approx. Reason., 2020

Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering.
Eng. Appl. Artif. Intell., 2020

Autoregressive Asymmetric Linear Gaussian Hidden Markov Models.
CoRR, 2020

Incremental Learning of Latent Forests.
IEEE Access, 2020

Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data.
IEEE Access, 2020

2019
Tractable learning of Bayesian networks from partially observed data.
Pattern Recognit., 2019

A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks.
Inf. Sci., 2019

Circular Bayesian classifiers using wrapped Cauchy distributions.
Data Knowl. Eng., 2019

Learning tractable Bayesian networks in the space of elimination orders.
Artif. Intell., 2019

A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas.
IEEE Access, 2019

Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors.
IEEE Access, 2019

2018
bnclassify: Learning Bayesian Network Classifiers.
R J., 2018

3D morphology-based clustering and simulation of human pyramidal cell dendritic spines.
PLoS Comput. Biol., 2018

A regularity index for dendrites - local statistics of a neuron's input space.
PLoS Comput. Biol., 2018

Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes.
IEEE Internet Things J., 2018

Tractability of most probable explanations in multidimensional Bayesian network classifiers.
Int. J. Approx. Reason., 2018

Markov Property in Generative Classifiers.
CoRR, 2018

Towards a supervised classification of neocortical interneuron morphologies.
BMC Bioinform., 2018

Discrete model-based clustering with overlapping subsets of attributes.
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Learning Bayesian network classifiers with completed partially directed acyclic graphs.
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

A partial orthogonalization method for simulating covariance and concentration graph matrices.
Proceedings of the International Conference on Probabilistic Graphical Models, 2018

Multi-dimensional Bayesian Network Classifier Trees.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018

A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices.
Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2018, 2018

Asymmetric Hidden Markov Models with Continuous Variables.
Proceedings of the Advances in Artificial Intelligence, 2018

Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks.
Proceedings of the Advances in Artificial Intelligence, 2018

2017
Univariate and bivariate truncated von Mises distributions.
Prog. Artif. Intell., 2017

Frobenius Norm Regularization for the Multivariate Von Mises Distribution.
Int. J. Intell. Syst., 2017

Network design through forests with degree- and role-constrained minimum spanning trees.
J. Heuristics, 2017

Architecture for anomaly detection in a laser heating surface process.
Proceedings of the 22nd IEEE International Conference on Emerging Technologies and Factory Automation, 2017

2016
Learning Bayesian networks with low inference complexity.
Prog. Artif. Intell., 2016

Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons.
Neuroinformatics, 2016

Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices.
J. Assoc. Inf. Sci. Technol., 2016

Decision functions for chain classifiers based on Bayesian networks for multi-label classification.
Int. J. Approx. Reason., 2016

Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers.
Intell. Data Anal., 2016

A review of undirected and acyclic directed Gaussian Markov model selection and estimation.
CoRR, 2016

Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot.
Proceedings of the STAIRS 2016, 2016

Learning Tractable Multidimensional Bayesian Network Classifiers.
Proceedings of the Probabilistic Graphical Models - Eighth International Conference, 2016

Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment.
Proceedings of the Machine Learning for Cyber Physical Systems, 2016

Hybrid Gaussian and von Mises Model-Based Clustering.
Proceedings of the ECAI 2016 - 22nd European Conference on Artificial Intelligence, 29 August-2 September 2016, The Hague, The Netherlands, 2016

Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions.
Proceedings of the Advances in Artificial Intelligence, 2016

2015
A survey on multi-output regression.
Wiley Interdiscip. Rev. Data Min. Knowl. Discov., 2015

Directional naive Bayes classifiers.
Pattern Anal. Appl., 2015

Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons.
Neuroinformatics, 2015

Decision boundary for discrete Bayesian network classifiers.
J. Mach. Learn. Res., 2015

Conditional Density Approximations with Mixtures of Polynomials.
Int. J. Intell. Syst., 2015

Interval-based ranking in noisy evolutionary multi-objective optimization.
Comput. Optim. Appl., 2015

Classifying GABAergic interneurons with semi-supervised projected model-based clustering.
Artif. Intell. Medicine, 2015

Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control.
Proceedings of the Machine Learning for Cyber Physical Systems, 2015

Towards Gaussian Bayesian Network Fusion.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2015

Regularized Multivariate von Mises Distribution.
Proceedings of the Advances in Artificial Intelligence, 2015

2014
Multi-Dimensional Classification with Super-Classes.
IEEE Trans. Knowl. Data Eng., 2014

Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables.
IEEE Trans. Evol. Comput., 2014

Multi-label classification with Bayesian network-based chain classifiers.
Pattern Recognit. Lett., 2014

Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals.
Neurocomputing, 2014

Bayesian network modeling of the consensus between experts: An application to neuron classification.
Int. J. Approx. Reason., 2014

Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation.
Int. J. Approx. Reason., 2014

Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty.
Frontiers Comput. Neurosci., 2014

Bayesian networks in neuroscience: a survey.
Frontiers Comput. Neurosci., 2014

Semi-supervised projected model-based clustering.
Data Min. Knowl. Discov., 2014

Discrete Bayesian Network Classifiers: A Survey.
ACM Comput. Surv., 2014

Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification.
Proceedings of the Probabilistic Graphical Models - 7th European Workshop, 2014

2013
Cluster methods for assessing research performance: exploring Spanish computer science.
Scientometrics, 2013

Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 2000-2009.
Scientometrics, 2013

Bayesian Sparse Partial Least Squares.
Neural Comput., 2013

Parameter Control of Genetic Algorithms by Learning and Simulation of Bayesian Networks - A Case Study for the Optimal Ordering of Tables.
J. Comput. Sci. Technol., 2013

A review on evolutionary algorithms in Bayesian network learning and inference tasks.
Inf. Sci., 2013

Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data.
Inf. Sci., 2013

Classification of neural signals from sparse autoregressive features.
Neurocomputing, 2013

Network measures for information extraction in evolutionary algorithms.
Int. J. Comput. Intell. Syst., 2013

An L<sub>1</sub>-Regularized naïVE Bayes-Inspired Classifier for Discarding Redundant and Irrelevant Predictors.
Int. J. Artif. Intell. Tools, 2013

Sparse regularized local regression.
Comput. Stat. Data Anal., 2013

Combinatorial Optimization by Learning and Simulation of Bayesian Networks
CoRR, 2013

A new measure for gene expression biclustering based on non-parametric correlation.
Comput. Methods Programs Biomed., 2013

Regularized continuous estimation of distribution algorithms.
Appl. Soft Comput., 2013

Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers.
Artif. Intell. Medicine, 2013

Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach.
Artif. Intell. Medicine, 2013

Towards optimal neuronal wiring through estimation of distribution algorithms.
Proceedings of the Genetic and Evolutionary Computation Conference, 2013

Bayesian networks to answer challenging neuroscience questions.
Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, 2013

Augmented Semi-naive Bayes Classifier.
Proceedings of the Advances in Artificial Intelligence, 2013

Learning Mixtures of Polynomials of Conditional Densities from Data.
Proceedings of the Advances in Artificial Intelligence, 2013

Learning Conditional Linear Gaussian Classifiers with Probabilistic Class Labels.
Proceedings of the Advances in Artificial Intelligence, 2013

Semi-supervised Projected Clustering for Classifying GABAergic Interneurons.
Proceedings of the Artificial Intelligence in Medicine, 2013

2012
Forward stagewise naïve Bayes.
Prog. Artif. Intell., 2012

Wrapper positive Bayesian network classifiers.
Knowl. Inf. Syst., 2012

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

A comparison of clustering quality indices using outliers and noise.
Intell. Data Anal., 2012

A review on probabilistic graphical models in evolutionary computation.
J. Heuristics, 2012

Lazy lasso for local regression.
Comput. Stat., 2012

Ensemble transcript interaction networks: A case study on Alzheimer's disease.
Comput. Methods Programs Biomed., 2012

Regularized logistic regression and multiobjective variable selection for classifying MEG data.
Biol. Cybern., 2012

Maximizing the number of polychronous groups in spiking networks.
Proceedings of the Genetic and Evolutionary Computation Conference, 2012

2011
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms.
IEEE ACM Trans. Comput. Biol. Bioinform., 2011

Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals.
Scientometrics, 2011

Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation.
Neuroinformatics, 2011

Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks.
Neuroinformatics, 2011

Multi-dimensional classification with Bayesian networks.
Int. J. Approx. Reason., 2011

Classifying evolving data streams with partially labeled data.
Intell. Data Anal., 2011

Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms.
J. Heuristics, 2011

Regularized logistic regression without a penalty term: An application to cancer classification with microarray data.
Expert Syst. Appl., 2011

Probabilistic graphical models in artificial intelligence.
Appl. Soft Comput., 2011

Predicting the h-index with cost-sensitive naive Bayes.
Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011

Bayesian Chain Classifiers for Multidimensional Classification.
Proceedings of the IJCAI 2011, 2011

Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods.
Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Regularized k-order markov models in EDAs.
Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Affinity propagation enhanced by estimation of distribution algorithms.
Proceedings of the 13th Annual Genetic and Evolutionary Computation Conference, 2011

Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables.
Proceedings of the Evolutionary Multi-Criterion Optimization, 2011

The von Mises Naive Bayes Classifier for Angular Data.
Proceedings of the Advances in Artificial Intelligence, 2011

2010
Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space.
IEEE Trans. Syst. Man Cybern. Part B, 2010

Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables.
Expert Syst. Appl., 2010

Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation.
Evol. Comput., 2010

Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience.
Proceedings of the Trends in Applied Intelligent Systems, 2010

Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances.
Proceedings of the Trends in Applied Intelligent Systems, 2010

Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models.
Proceedings of the Evolutionary Computation, 2010

Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms.
Proceedings of the IEEE Congress on Evolutionary Computation, 2010

EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization.
Proceedings of the Swarm Intelligence - 7th International Conference, 2010

2009
Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures.
IEEE Trans. Inf. Technol. Biomed., 2009

Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models.
IEEE Trans. Evol. Comput., 2009

Feature subset selection from positive and unlabelled examples.
Pattern Recognit. Lett., 2009

Research topics in discrete estimation of distribution algorithms based on factorizations.
Memetic Comput., 2009

Triangulation of Bayesian networks with recursive estimation of distribution algorithms.
Int. J. Approx. Reason., 2009

Bayesian classifiers based on kernel density estimation: Flexible classifiers.
Int. J. Approx. Reason., 2009

Predicting citation count of <i>Bioinformatics</i> papers within four years of publication.
Bioinform., 2009

Probabilistic Graphical Markov Model Learning: An Adaptive Strategy.
Proceedings of the MICAI 2009: Advances in Artificial Intelligence, 2009

Mining probabilistic models learned by EDAs in the optimization of multi-objective problems.
Proceedings of the Genetic and Evolutionary Computation Conference, 2009

2008
Adaptive Estimation of Distribution Algorithms.
Proceedings of the Adaptive and Multilevel Metaheuristics, 2008

The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks.
Proceedings of the Linkage in Evolutionary Computation, 2008

Protein Folding in Simplified Models With Estimation of Distribution Algorithms.
IEEE Trans. Evol. Comput., 2008

Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering.
J. Comput. Biol., 2008

Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem.
J. Heuristics, 2008

Bayesian classification for the selection of in vitro human embryos using morphological and clinical data.
Comput. Methods Programs Biomed., 2008

Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers.
Comput. Methods Programs Biomed., 2008

Selection of human embryos for transfer by Bayesian classifiers.
Comput. Biol. Medicine, 2008

A review of estimation of distribution algorithms in bioinformatics.
BioData Min., 2008

Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs.
Proceedings of the Parallel Problem Solving from Nature, 2008

Component weighting functions for adaptive search with EDAs.
Proceedings of the IEEE Congress on Evolutionary Computation, 2008

2007
Learning Bayesian classifiers from positive and unlabeled examples.
Pattern Recognit. Lett., 2007

Wrapper discretization by means of estimation of distribution algorithms.
Intell. Data Anal., 2007

Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains.
Connect. Sci., 2007

A partially supervised classification approach to dominant and recessive human disease gene prediction.
Comput. Methods Programs Biomed., 2007

A review of feature selection techniques in bioinformatics.
Bioinform., 2007

Side chain placement using estimation of distribution algorithms.
Artif. Intell. Medicine, 2007

The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms.
Proceedings of the Evolutionary Computation, 2007

Discriminative vs. Generative Learning of Bayesian Network Classifiers.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2007

Exact Bayesian network learning in estimation of distribution algorithms.
Proceedings of the IEEE Congress on Evolutionary Computation, 2007

2006
Bayesian Model Averaging of Naive Bayes for Clustering.
IEEE Trans. Syst. Man Cybern. Part B, 2006

Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes.
Int. J. Approx. Reason., 2006

Machine learning in bioinformatics.
Briefings Bioinform., 2006

Discriminative Learning of Bayesian Network Classifiers.
Inteligencia Artif., 2006

Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains.
Proceedings of the Simulated Evolution and Learning, 6th International Conference, 2006

Bayesian Model Averaging of TAN Models for Clustering.
Proceedings of the Third European Workshop on Probabilistic Graphical Models, 2006

Mixtures of Kikuchi Approximations.
Proceedings of the Machine Learning: ECML 2006, 2006

Information Theory and Classification Error in Probabilistic Classifiers.
Proceedings of the Discovery Science, 9th International Conference, 2006

GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm.
Proceedings of the Towards a New Evolutionary Computation, 2006

Bayesian Classifiers in Optimization: An EDA-like Approach.
Proceedings of the Towards a New Evolutionary Computation, 2006

2005
Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms.
Pattern Recognit., 2005

Editorial.
Mach. Learn., 2005

Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS.
J. Biomed. Informatics, 2005

Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks.
Evol. Comput., 2005

Editorial Introduction Special Issue on Estimation of Distribution Algorithms.
Evol. Comput., 2005

Average Time Complexity of Estimation of Distribution Algorithms.
Proceedings of the Computational Intelligence and Bioinspired Systems, 2005

Discriminative Learning of Bayesian Network Classifiers via the TM Algorithm.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning with Uncertainty, 2005

Interactions and dependencies in estimation of distribution algorithms.
Proceedings of the IEEE Congress on Evolutionary Computation, 2005

A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models.
Proceedings of the Data Analysis and Visualization in Genomics and Proteomics, 2005

2004
Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering.
Int. J. Uncertain. Fuzziness Knowl. Based Syst., 2004

Learning Bayesian Networks In The Space Of Orderings With Estimation Of Distribution Algorithms.
Int. J. Pattern Recognit. Artif. Intell., 2004

Gene Selection For Cancer Classification Using Wrapper Approaches.
Int. J. Pattern Recognit. Artif. Intell., 2004

Bayesian network multi-classifiers for protein secondary structure prediction.
Artif. Intell. Medicine, 2004

Filter versus wrapper gene selection approaches in DNA microarray domains.
Artif. Intell. Medicine, 2004

Protein Folding in 2-Dimensional Lattices with Estimation of Distribution Algorithms.
Proceedings of the Biological and Medical Data Analysis, 5th International Symposium, 2004

Selective Classifiers Can Be Too Restrictive: A Case-Study in Oesophageal Cancer.
Proceedings of the Biological and Medical Data Analysis, 5th International Symposium, 2004

GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms.
Proceedings of the Innovations in Applied Artificial Intelligence, 2004

2003
Learning Bayesian networks in the space of structures by estimation of distribution algorithms.
Int. J. Intell. Syst., 2003

Algoritmos de Estimación de Distribuciones en Problemas de Optimización Combinatoria.
Inteligencia Artif., 2003

Improvement of Naïve Bayes Collaborative Filtering Using Interval Estimation.
Proceedings of the 2003 IEEE / WIC International Conference on Web Intelligence, 2003

Parallel Stochastic Search for Protein Secondary Structure Prediction.
Proceedings of the Parallel Processing and Applied Mathematics, 2003

Analysis of the Univariate Marginal Distribution Algorithm Modeled by Markov Chains.
Proceedings of the Artificial Neural Nets Problem Solving Methods, 2003

Interval Estimation Naïve Bayes.
Proceedings of the Advances in Intelligent Data Analysis V, 2003

Learning Semi Naïve Bayes Structures by Estimation of Distribution Algorithms.
Proceedings of the Progress in Artificial Intelligence, 2003

Collaborative Filtering Using Interval Estimation Naïve Bayes.
Proceedings of the Web Intelligence, 2003

2002
Inexact graph matching by means of estimation of distribution algorithms.
Pattern Recognit., 2002

Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction.
Mach. Learn., 2002

Synergies between evolutionary computation and probabilistic graphical models.
Int. J. Approx. Reason., 2002

Mathematical modelling of UMDA<sub>c</sub> algorithm with tournament selection. Behaviour on linear and quadratic functions.
Int. J. Approx. Reason., 2002

Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms.
Proceedings of the First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002, 2002

Floating Search Methods in Learning Bayesian Networks.
Proceedings of the First European Workshop on Probabilistic Graphical Models, 6-8 November - 2002, 2002

Rule Induction by Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

Solving the 0-1 Knapsack Problem with EDAs.
Proceedings of the Estimation of Distribution Algorithms, 2002

An Empirical Comparison Between K-Means, GAs and EDAs in Partitional Clustering.
Proceedings of the Estimation of Distribution Algorithms, 2002

Solving the Traveling Salesman Problem with EDAs.
Proceedings of the Estimation of Distribution Algorithms, 2002

Benefits of Data Clustering in Multimodal Function Optimization via EDAs.
Proceedings of the Estimation of Distribution Algorithms, 2002

Parallel Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

A Review on Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

An Introduction to Probabilistic Graphical Models.
Proceedings of the Estimation of Distribution Algorithms, 2002

Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

Feature Subset Selection by Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

Mathematical Modeling of Discrete Estimation of Distribution Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms.
Proceedings of the Estimation of Distribution Algorithms, 2002

Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs.
Proceedings of the Estimation of Distribution Algorithms, 2002

Experimental Results in Function Optimization with EDAs in Continuous Domain.
Proceedings of the Estimation of Distribution Algorithms, 2002

Solving Graph Matching with EDAs Using a Permutation-Based Representation.
Proceedings of the Estimation of Distribution Algorithms, 2002

2001
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks.
IEEE Trans. Pattern Anal. Mach. Intell., 2001

Performance evaluation of compromise conditional Gaussian networks for data clustering.
Int. J. Approx. Reason., 2001

Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms.
Int. J. Approx. Reason., 2001

Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data.
Artif. Intell. Medicine, 2001

Feature subset selection by genetic algorithms and estimation of distribution algorithms - A case study in the survival of cirrhotic patients treated with TIPS.
Artif. Intell. Medicine, 2001

On Applying Supervised Classification Techniques in Medicine.
Proceedings of the Medical Data Analysis, Second International Symposium, 2001

Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems.
Proceedings of the Energy Minimization Methods in Computer Vision and Pattern Recognition, 2001

Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks.
Proceedings of the Eighth International Workshop on Artificial Intelligence and Statistics, 2001

Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS.
Proceedings of the Artificial Intelligence Medicine, 2001

2000
An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering.
Pattern Recognit. Lett., 2000

Analyzing the Population Based Incremental Learning Algorithm by Means of Discrete Dynamical Systems.
Complex Syst., 2000

Feature Subset Selection by Bayesian network-based optimization.
Artif. Intell., 2000

Combinatonal Optimization by Learning and Simulation of Bayesian Networks.
Proceedings of the UAI '00: Proceedings of the 16th Conference in Uncertainty in Artificial Intelligence, Stanford University, Stanford, California, USA, June 30, 2000

Medical Bayes Networks.
Proceedings of the Medical Data Analysis, First International Symposium, 2000

Feature Subset Selection Using Probabilistic Tree Structures. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS.
Proceedings of the Medical Data Analysis, First International Symposium, 2000

1999
Genetic Algorithms: Bridging the Convergence Gap.
Theor. Comput. Sci., 1999

Learning Bayesian networks for clustering by means of constructive induction.
Pattern Recognit. Lett., 1999

An empirical comparison of four initialization methods for the K-Means algorithm.
Pattern Recognit. Lett., 1999

Applying genetic algorithms to search for the best hierarchical clustering of a dataset.
Pattern Recognit. Lett., 1999

Representing the behaviour of supervised classification learning algorithms by Bayesian networks.
Pattern Recognit. Lett., 1999

Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators.
Artif. Intell. Rev., 1999

Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit.
Proceedings of the Artificial Intelligence in Medicine. Joint European Conference on Artificial Intelligence in Medicine and Medical Decision Making, 1999

1998
Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches.
Artif. Intell. Medicine, 1998

Aplicación de los algoritmos genéticos al problema del clustering jerárquico.
Inteligencia Artif., 1998

1997
Structure of the high-order Boltzmann machine from independence maps.
IEEE Trans. Neural Networks, 1997

Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms.
Stat. Comput., 1997

Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data.
Pattern Recognit. Lett., 1997

Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering.
Proceedings of the International Conference on Artificial Neural Nets and Genetic Algorithms, 1997

Bayesian Networks, Rule Induction and Logistic Regression in the Prediction of the Survival of Women Suffering from Breast Cancer.
Proceedings of the Progress in Artificial Intelligence, 1997

Learning Bayesisan Networks by Genetic Algorithms: A Case Study in the Prediction of Survival in Malignant Skin Melanoma.
Proceedings of the Artificial Intelligence Medicine, 1997

1996
Learning Bayesian network structures by searching for the best ordering with genetic algorithms.
IEEE Trans. Syst. Man Cybern. Part A, 1996

Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters.
IEEE Trans. Pattern Anal. Mach. Intell., 1996

1995
Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms.
Proceedings of the Learning from Data, 1995

1993
Structure learning approaches in Causal Probalistics Networks.
Proceedings of the Symbolic and Quantitative Approaches to Reasoning and Uncertainty, 1993

Genetic Algorithms Elitist Probabilistic of Degree 1, a generalization of Simulated Annealing.
Proceedings of the Advances in Artificial Intelligence, 1993


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