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ORCIDPedro Larrañaga
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- affiliation: Universidad Politécnica de Madrid, Spain
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2020 – today
- 2024
[j160]Vicente P. Soloviev
, Pedro Larrañaga, Concha Bielza:
EDAspy: An extensible python package for estimation of distribution algorithms. Neurocomputing 598: 128043 (2024)- [j159]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Semiparametric Estimation of Distribution Algorithms for Continuous Optimization. IEEE Trans. Evol. Comput. 28(4): 1069-1083 (2024) - [j158]Pedro Larrañaga, Concha Bielza:
Estimation of Distribution Algorithms in Machine Learning: A Survey. IEEE Trans. Evol. Comput. 28(5): 1301-1321 (2024) - [j157]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Feature Saliencies in Asymmetric Hidden Markov Models. IEEE Trans. Neural Networks Learn. Syst. 35(3): 3586-3600 (2024) - 2023
- [j156]Carlos Villa-Blanco, Concha Bielza, Pedro Larrañaga:
Feature subset selection for data and feature streams: a review. Artif. Intell. Rev. 56(S1): 1011-1062 (2023) - [j155]Gabriel Valverde, David Quesada, Pedro Larrañaga, Concha Bielza:
Causal reinforcement learning based on Bayesian networks applied to industrial settings. Eng. Appl. Artif. Intell. 125: 106657 (2023) - [j154]Carlos Villa-Blanco, Alessandro Bregoli, Concha Bielza, Pedro Larrañaga, Fabio Stella:
Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers. Int. J. Approx. Reason. 159: 108945 (2023) - [j153]Enrique Valero-Leal, Concha Bielza, Pedro Larrañaga, Silja Renooij:
Efficient search for relevance explanations using MAP-independence in Bayesian networks. Int. J. Approx. Reason. 160: 108965 (2023) - [j152]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Feature subset selection in data-stream environments using asymmetric hidden Markov models and novelty detection. Neurocomputing 554: 126641 (2023) - [j151]Nikolas Bernaola, Mario Michiels, Pedro Larrañaga, Concha Bielza:
Learning massive interpretable gene regulatory networks of the human brain by merging Bayesian networks. PLoS Comput. Biol. 19(12) (2023) - [j150]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Quantum approximate optimization algorithm for Bayesian network structure learning. Quantum Inf. Process. 22(1): 19 (2023) - [c86]Vicente P. Soloviev, Pedro Larrañaga, Concha Bielza:
Variational Quantum Algorithm Parameter Tuning with Estimation of Distribution Algorithms. CEC 2023: 1-9 - [c85]Dafne Lozano, Luis Bote, Concha Bielza, Pedro Larrañaga, María Sabater-Molina, Juan Ramón Gimeno, Sergio Muñoz, Francisco Javier Gimeno-Blanes, José Luis Rojo-Álvarez:
High-Dimensional Feature Characterization of Single Nucleotide Variants in Hypertrophic Cardiomyopathy. CinC 2023: 1-4 - [c84]Jorge Casajús-Setién, Concha Bielza, Pedro Larrañaga:
Anomaly-Based Intrusion Detection in IIoT Networks Using Transformer Models. CSR 2023: 72-77 - [i11]Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga, Gustav Eje Henter:
Context-specific kernel-based hidden Markov model for time series analysis. CoRR abs/2301.09870 (2023) - [i10]David Quesada, Pedro Larrañaga, Concha Bielza:
Classifying the evolution of COVID-19 severity on patients with combined dynamic Bayesian networks and neural networks. CoRR abs/2303.05972 (2023) - 2022
- [j149]Fernando Rodriguez-Sanchez, Concha Bielza, Pedro Larrañaga:
Multipartition clustering of mixed data with Bayesian networks. Int. J. Intell. Syst. 37(3): 2188-2218 (2022) - [j148]David Quesada, Concha Bielza, Pedro Fontán, Pedro Larrañaga:
Piecewise forecasting of nonlinear time series with model tree dynamic Bayesian networks. Int. J. Intell. Syst. 37(11): 9108-9137 (2022) - [j147]David Atienza, Concha Bielza, Pedro Larrañaga:
PyBNesian: An extensible python package for Bayesian networks. Neurocomputing 504: 204-209 (2022) - [j146]Carlos Puerto-Santana, Concha Bielza, Javier Diaz-Rozo, Guillem Ramirez-Gargallo, Filippo Mantovani, Gaizka Virumbrales, Jesús Labarta, Pedro Larrañaga:
Asymmetric HMMs for Online Ball-Bearing Health Assessments. IEEE Internet Things J. 9(20): 20160-20177 (2022) - [j145]David Atienza, Concha Bielza, Pedro Larrañaga:
Semiparametric Bayesian networks. Inf. Sci. 584: 564-582 (2022) - [j144]Vicente P. Soloviev, Pedro Larrañaga, Concha Bielza:
Estimation of distribution algorithms using Gaussian Bayesian networks to solve industrial optimization problems constrained by environment variables. J. Comb. Optim. 44(2): 1077-1098 (2022) - [j143]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models. IEEE Trans. Pattern Anal. Mach. Intell. 44(9): 4642-4658 (2022) - [c83]Vicente P. Soloviev, Pedro Larrañaga, Concha Bielza:
Quantum parametric circuit optimization with estimation of distribution algorithms. GECCO Companion 2022: 2247-2250 - [c82]Carlos Villa-Blanco, Alessandro Bregoli, Concha Bielza, Pedro Larrañaga, Fabio Stella:
Structure learning algorithms for multidimensional continuous-time Bayesian network classifiers. PGM 2022: 313-324 - [c81]Enrique Valero-Leal, Pedro Larrañaga, Concha Bielza:
Interpreting Time-Varying Dynamic Bayesian Networks for Earth Climate Modelling. PGM 2022: 373-384 - [c80]Jorge Casajús-Setién, Concha Bielza, Pedro Larrañaga:
Evolutive Adversarially-Trained Bayesian Network Autoencoder for Interpretable Anomaly Detection. PGM 2022: 397-408 - [d1]David Atienza, Concha Bielza, Javier Diaz-Rozo, Pedro Larrañaga:
Anomaly Detection with Laser Heat Treatment Thermal Videos. IEEE DataPort, 2022
- [i9]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Quantum Approximate Optimization Algorithm for Bayesian network structure learning. CoRR abs/2203.02400 (2022) - 2021
- [j142]Santiago Gil-Begue, Concha Bielza, Pedro Larrañaga:
Multi-dimensional Bayesian network classifiers: A survey. Artif. Intell. Rev. 54(1): 519-559 (2021) - [j141]David Quesada, Gabriel Valverde, Pedro Larrañaga, Concha Bielza:
Long-term forecasting of multivariate time series in industrial furnaces with dynamic Gaussian Bayesian networks. Eng. Appl. Artif. Intell. 103: 104301 (2021) - [j140]Bojan Mihaljevic, Pedro Larrañaga, Concha Bielza:
Comparing the Electrophysiology and Morphology of Human and Mouse Layer 2/3 Pyramidal Neurons With Bayesian Networks. Frontiers Neuroinformatics 15: 580873 (2021) - [j139]Carlos Villa-Blanco, Pedro Larrañaga, Concha Bielza:
Multidimensional continuous time Bayesian network classifiers. Int. J. Intell. Syst. 36(12): 7839-7866 (2021) - [j138]Mario Michiels, Pedro Larrañaga, Concha Bielza:
BayeSuites: An open web framework for massive Bayesian networks focused on neuroscience. Neurocomputing 428: 166-181 (2021) - [j137]Bojan Mihaljevic, Concha Bielza, Pedro Larrañaga:
Bayesian networks for interpretable machine learning and optimization. Neurocomputing 456: 648-665 (2021) - [c79]Vicente P. Soloviev, Concha Bielza, Pedro Larrañaga:
Quantum-Inspired Estimation Of Distribution Algorithm To Solve The Travelling Salesman Problem. CEC 2021: 416-425 - [c78]David Quesada, Concha Bielza, Pedro Larrañaga:
Structure Learning of High-Order Dynamic Bayesian Networks via Particle Swarm Optimization with Order Invariant Encoding. HAIS 2021: 158-171 - [c77]Carlos Puerto-Santana, Pedro Larrañaga, Javier Diaz-Rozo, Concha Bielza:
An Online Feature Selection Methodology for Ball-Bearing Harmonic Frequencies Based on HMMs. SOCO 2021: 546-555 - [i8]David Atienza, Concha Bielza, Pedro Larrañaga:
Semiparametric Bayesian Networks. CoRR abs/2109.03008 (2021) - 2020
- [j136]Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando:
Sparse Cholesky Covariance Parametrization for Recovering Latent Structure in Ordered Data. IEEE Access 8: 154614-154624 (2020) - [j135]Fernando Rodriguez-Sanchez, Pedro Larrañaga, Concha Bielza:
Incremental Learning of Latent Forests. IEEE Access 8: 224420-224432 (2020) - [j134]Javier Diaz-Rozo, Concha Bielza, Pedro Larrañaga:
Machine-tool condition monitoring with Gaussian mixture models-based dynamic probabilistic clustering. Eng. Appl. Artif. Intell. 89: 103434 (2020) - [j133]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
On generating random Gaussian graphical models. Int. J. Approx. Reason. 125: 240-250 (2020) - [c76]Nikolas Bernaola, Mario Michiels, Concha Bielza, Pedro Larrañaga:
BayesSuites: An Open Web Framework for Visualization of Massive Bayesian Networks. PGM 2020: 593-596 - [i7]Irene Córdoba, Concha Bielza, Pedro Larrañaga, Gherardo Varando:
Sparse Cholesky covariance parametrization for recovering latent structure in ordered data. CoRR abs/2006.01448 (2020) - [i6]Carlos Puerto-Santana, Pedro Larrañaga, Concha Bielza:
Autoregressive Asymmetric Linear Gaussian Hidden Markov Models. CoRR abs/2010.15604 (2020)
2010 – 2019
- 2019
- [j132]Pablo Fernandez-Gonzalez, Concepcion Bielza, Pedro Larrañaga:
Random Forests for Regression as a Weighted Sum of ${k}$ -Potential Nearest Neighbors. IEEE Access 7: 25660-25672 (2019) - [j131]Sergio Luengo-Sanchez, Pedro Larrañaga, Concha Bielza:
A Directional-Linear Bayesian Network and Its Application for Clustering and Simulation of Neural Somas. IEEE Access 7: 69907-69921 (2019) - [j130]Marco Benjumeda, Concha Bielza, Pedro Larrañaga:
Learning tractable Bayesian networks in the space of elimination orders. Artif. Intell. 274: 66-90 (2019) - [j129]Ignacio Leguey, Concha Bielza, Pedro Larrañaga:
Circular Bayesian classifiers using wrapped Cauchy distributions. Data Knowl. Eng. 122: 101-115 (2019) - [j128]Ignacio Leguey, Pedro Larrañaga, Concha Bielza, Shogo Kato:
A circular-linear dependence measure under Johnson-Wehrly distributions and its application in Bayesian networks. Inf. Sci. 486: 240-253 (2019) - [j127]Marco Benjumeda, Sergio Luengo-Sanchez, Pedro Larrañaga, Concha Bielza:
Tractable learning of Bayesian networks from partially observed data. Pattern Recognit. 91: 190-199 (2019) - 2018
- [j126]Bojan Mihaljevic, Pedro Larrañaga, Ruth Benavides-Piccione, Sean L. Hill, Javier DeFelipe, Concha Bielza:
Towards a supervised classification of neocortical interneuron morphologies. BMC Bioinform. 19(1): 511:1-511:22 (2018) - [j125]Marco Benjumeda, Concha Bielza, Pedro Larrañaga:
Tractability of most probable explanations in multidimensional Bayesian network classifiers. Int. J. Approx. Reason. 93: 74-87 (2018) - [j124]Javier Diaz-Rozo, Concha Bielza, Pedro Larrañaga:
Clustering of Data Streams With Dynamic Gaussian Mixture Models: An IoT Application in Industrial Processes. IEEE Internet Things J. 5(5): 3533-3547 (2018) - [j123]Sergio Luengo-Sanchez, Isabel Fernaud, Concha Bielza, Ruth Benavides-Piccione, Pedro Larrañaga, Javier DeFelipe:
3D morphology-based clustering and simulation of human pyramidal cell dendritic spines. PLoS Comput. Biol. 14(6) (2018) - [j122]Laura Anton-Sanchez, Felix Effenberger, Concha Bielza, Pedro Larrañaga, Hermann Cuntz:
A regularity index for dendrites - local statistics of a neuron's input space. PLoS Comput. Biol. 14(11) (2018) - [j121]Bojan Mihaljevic, Concha Bielza, Pedro Larrañaga:
bnclassify: Learning Bayesian Network Classifiers. R J. 10(2): 455 (2018) - [c75]Irene Córdoba, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga:
Bayesian Optimization of the PC Algorithm for Learning Gaussian Bayesian Networks. CAEPIA 2018: 44-54 - [c74]Carlos Puerto-Santana, Concha Bielza, Pedro Larrañaga:
Asymmetric Hidden Markov Models with Continuous Variables. CAEPIA 2018: 98-107 - [c73]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A Fast Metropolis-Hastings Method for Generating Random Correlation Matrices. IDEAL (1) 2018: 117-124 - [c72]Santiago Gil-Begue, Pedro Larrañaga, Concha Bielza:
Multi-dimensional Bayesian Network Classifier Trees. IDEAL (1) 2018: 354-363 - [c71]Irene Córdoba, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A partial orthogonalization method for simulating covariance and concentration graph matrices. PGM 2018: 61-72 - [c70]Bojan Mihaljevic, Concha Bielza, Pedro Larrañaga:
Learning Bayesian network classifiers with completed partially directed acyclic graphs. PGM 2018: 272-283 - [c69]Fernando Rodriguez-Sanchez, Pedro Larrañaga, Concha Bielza:
Discrete model-based clustering with overlapping subsets of attributes. PGM 2018: 392-403 - [i5]Irene Córdoba-Sánchez, Eduardo C. Garrido-Merchán, Daniel Hernández-Lobato, Concha Bielza, Pedro Larrañaga:
Bayesian optimization of the PC algorithm for learning Gaussian Bayesian networks. CoRR abs/1806.11015 (2018) - [i4]Gherardo Varando, Concha Bielza, Pedro Larrañaga, Eva Riccomagno:
Markov Property in Generative Classifiers. CoRR abs/1811.04759 (2018) - [i3]Irene Córdoba, Concha Bielza, Pedro Larrañaga:
Towards Gaussian Bayesian Network Fusion. CoRR abs/1812.00262 (2018) - 2017
- [j120]Laura Anton-Sanchez, Concha Bielza, Pedro Larrañaga:
Network design through forests with degree- and role-constrained minimum spanning trees. J. Heuristics 23(1): 31-51 (2017) - [j119]Luis Rodriguez-Lujan, Pedro Larrañaga, Concha Bielza:
Frobenius Norm Regularization for the Multivariate Von Mises Distribution. Int. J. Intell. Syst. 32(2): 153-176 (2017) - [j118]Pablo Fernandez-Gonzalez, Concha Bielza, Pedro Larrañaga:
Univariate and bivariate truncated von Mises distributions. Prog. Artif. Intell. 6(2): 171-180 (2017) - [c68]Javier Mesonero, Concha Bielza, Pedro Larrañaga:
Architecture for anomaly detection in a laser heating surface process. ETFA 2017: 1-4 - 2016
- [j117]Hanen Borchani, Pedro Larrañaga, João Gama, Concha Bielza:
Mining multi-dimensional concept-drifting data streams using Bayesian network classifiers. Intell. Data Anal. 20(2): 257-280 (2016) - [j116]Gherardo Varando, Concha Bielza, Pedro Larrañaga:
Decision functions for chain classifiers based on Bayesian networks for multi-label classification. Int. J. Approx. Reason. 68: 164-178 (2016) - [j115]Alfonso Ibáñez, Rubén Armañanzas, Concha Bielza, Pedro Larrañaga:
Genetic algorithms and Gaussian Bayesian networks to uncover the predictive core set of bibliometric indices. J. Assoc. Inf. Sci. Technol. 67(7): 1703-1721 (2016) - [j114]Laura Anton-Sanchez, Concha Bielza, Ruth Benavides-Piccione, Javier DeFelipe, Pedro Larrañaga:
Dendritic and Axonal Wiring Optimization of Cortical GABAergic Interneurons. Neuroinformatics 14(4): 453-464 (2016) - [j113]Marco Benjumeda, Pedro Larrañaga, Concha Bielza:
Learning Bayesian networks with low inference complexity. Prog. Artif. Intell. 5(1): 15-26 (2016) - [c67]Ignacio Leguey, Concha Bielza, Pedro Larrañaga:
Tree-Structured Bayesian Networks for Wrapped Cauchy Directional Distributions. CAEPIA 2016: 207-216 - [c66]Sergio Luengo-Sanchez, Concha Bielza, Pedro Larrañaga:
Hybrid Gaussian and von Mises Model-Based Clustering. ECAI 2016: 855-862 - [c65]Alberto Ogbechie, Javier Diaz-Rozo, Pedro Larrañaga, Concha Bielza:
Dynamic Bayesian Network-Based Anomaly Detection for In-Process Visual Inspection of Laser Surface Heat Treatment. ML4CPS 2016: 17-24 - [c64]Marco Benjumeda, Concha Bielza, Pedro Larrañaga:
Learning Tractable Multidimensional Bayesian Network Classifiers. Probabilistic Graphical Models 2016: 13-24 - [c63]David Atienza, Concha Bielza, Javier Díaz, Pedro Larrañaga:
Anomaly Detection with a Spatio-Temporal Tracking of the Laser Spot. STAIRS 2016: 137-142 - [i2]Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga:
A review of undirected and acyclic directed Gaussian Markov model selection and estimation. CoRR abs/1606.07282 (2016) - 2015
- [j112]Bojan Mihaljevic, Ruth Benavides-Piccione, Luis Guerra, Javier DeFelipe, Pedro Larrañaga, Concha Bielza:
Classifying GABAergic interneurons with semi-supervised projected model-based clustering. Artif. Intell. Medicine 65(1): 49-59 (2015) - [j111]Hossein Karshenas, Concha Bielza, Pedro Larrañaga:
Interval-based ranking in noisy evolutionary multi-objective optimization. Comput. Optim. Appl. 61(2): 517-555 (2015) - [j110]Gherardo Varando, Pedro L. López-Cruz, Thomas D. Nielsen, Pedro Larrañaga, Concha Bielza:
Conditional Density Approximations with Mixtures of Polynomials. Int. J. Intell. Syst. 30(3): 236-264 (2015) - [j109]Gherardo Varando, Concha Bielza, Pedro Larrañaga:
Decision boundary for discrete Bayesian network classifiers. J. Mach. Learn. Res. 16: 2725-2749 (2015) - [j108]Bojan Mihaljevic, Ruth Benavides-Piccione, Concha Bielza, Javier DeFelipe, Pedro Larrañaga:
Bayesian Network Classifiers for Categorizing Cortical GABAergic Interneurons. Neuroinformatics 13(2): 193-208 (2015) - [j107]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
Directional naive Bayes classifiers. Pattern Anal. Appl. 18(2): 225-246 (2015) - [j106]Hanen Borchani, Gherardo Varando, Concha Bielza, Pedro Larrañaga:
A survey on multi-output regression. WIREs Data Mining Knowl. Discov. 5(5): 216-233 (2015) - [c62]Luis Rodriguez-Lujan, Concha Bielza, Pedro Larrañaga:
Regularized Multivariate von Mises Distribution. CAEPIA 2015: 25-35 - [c61]Irene Córdoba-Sánchez, Concha Bielza, Pedro Larrañaga:
Towards Gaussian Bayesian Network Fusion. ECSQARU 2015: 519-528 - [c60]Javier Díaz, Concha Bielza, Jose L. Ocaña, Pedro Larrañaga:
Development of a Cyber-Physical System based on selective Gaussian naïve Bayes model for a self-predict laser surface heat treatment process control. ML4CPS 2015: 1-8 - 2014
- [j105]Concha Bielza, Pedro Larrañaga:
Discrete Bayesian Network Classifiers: A Survey. ACM Comput. Surv. 47(1): 5:1-5:43 (2014) - [j104]Luis Guerra, Concha Bielza, Víctor Robles, Pedro Larrañaga:
Semi-supervised projected model-based clustering. Data Min. Knowl. Discov. 28(4): 882-917 (2014) - [j103]Concha Bielza, Pedro Larrañaga:
Bayesian networks in neuroscience: a survey. Frontiers Comput. Neurosci. 8: 131 (2014) - [j102]Bojan Mihaljevic, Concha Bielza, Ruth Benavides-Piccione, Javier DeFelipe, Pedro Larrañaga:
Multi-dimensional classification of GABAergic interneurons with Bayesian network-modeled label uncertainty. Frontiers Comput. Neurosci. 8: 150 (2014) - [j101]Pedro L. López-Cruz, Pedro Larrañaga, Javier DeFelipe, Concha Bielza:
Bayesian network modeling of the consensus between experts: An application to neuron classification. Int. J. Approx. Reason. 55(1): 3-22 (2014) - [j100]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation. Int. J. Approx. Reason. 55(4): 989-1010 (2014) - [j99]Alfonso Ibáñez, Concha Bielza, Pedro Larrañaga:
Cost-sensitive selective naive Bayes classifiers for predicting the increase of the h-index for scientific journals. Neurocomputing 135: 42-52 (2014) - [j98]Luis Enrique Sucar, Concha Bielza, Eduardo F. Morales, Pablo Hernandez-Leal, Julio H. Zaragoza, Pedro Larrañaga:
Multi-label classification with Bayesian network-based chain classifiers. Pattern Recognit. Lett. 41: 14-22 (2014) - [j97]Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larrañaga:
Multiobjective Estimation of Distribution Algorithm Based on Joint Modeling of Objectives and Variables. IEEE Trans. Evol. Comput. 18(4): 519-542 (2014) - [j96]Jesse Read, Concha Bielza, Pedro Larrañaga:
Multi-Dimensional Classification with Super-Classes. IEEE Trans. Knowl. Data Eng. 26(7): 1720-1733 (2014) - [c59]Gherardo Varando, Concha Bielza, Pedro Larrañaga:
Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification. Probabilistic Graphical Models 2014: 519-534 - 2013
- [j95]Hanen Borchani, Concha Bielza, Carlos Toro, Pedro Larrañaga:
Predicting human immunodeficiency virus inhibitors using multi-dimensional Bayesian network classifiers. Artif. Intell. Medicine 57(3): 219-229 (2013) - [j94]Rubén Armañanzas, Concha Bielza, Kallol Ray Chaudhuri, Pablo Martínez-Martín, Pedro Larrañaga:
Unveiling relevant non-motor Parkinson's disease severity symptoms using a machine learning approach. Artif. Intell. Medicine 58(3): 195-202 (2013) - [j93]Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larrañaga:
Regularized continuous estimation of distribution algorithms. Appl. Soft Comput. 13(5): 2412-2432 (2013) - [j92]Jose Luis Flores, Iñaki Inza, Pedro Larrañaga, Borja Calvo:
A new measure for gene expression biclustering based on non-parametric correlation. Comput. Methods Programs Biomed. 112(3): 367-397 (2013) - [j91]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Sparse regularized local regression. Comput. Stat. Data Anal. 62: 122-135 (2013) - [j90]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
An L1-Regularized naïVE Bayes-Inspired Classifier for Discarding Redundant and Irrelevant Predictors. Int. J. Artif. Intell. Tools 22(4) (2013) - [j89]Roberto Santana, Rubén Armañanzas, Concha Bielza, Pedro Larrañaga:
Network measures for information extraction in evolutionary algorithms. Int. J. Comput. Intell. Syst. 6(6): 1163-1188 (2013) - [j88]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Classification of neural signals from sparse autoregressive features. Neurocomputing 111: 21-26 (2013) - [j87]Miguel García-Torres, Rubén Armañanzas, Concha Bielza, Pedro Larrañaga:
Comparison of metaheuristic strategies for peakbin selection in proteomic mass spectrometry data. Inf. Sci. 222: 229-246 (2013) - [j86]Pedro Larrañaga, Hossein Karshenas, Concha Bielza, Roberto Santana:
A review on evolutionary algorithms in Bayesian network learning and inference tasks. Inf. Sci. 233: 109-125 (2013) - [j85]Concha Bielza, Juan A. Fernández del Pozo, Pedro Larrañaga:
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. 28(4): 720-731 (2013) - [j84]Diego Vidaurre, Marcel A. J. van Gerven, Concha Bielza, Pedro Larrañaga, Tom Heskes:
Bayesian Sparse Partial Least Squares. Neural Comput. 25(12): 3318-3339 (2013) - [j83]Alfonso Ibáñez, Concha Bielza, Pedro Larrañaga:
Relationship among research collaboration, number of documents and number of citations: a case study in Spanish computer science production in 2000-2009. Scientometrics 95(2): 689-716 (2013) - [j82]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Cluster methods for assessing research performance: exploring Spanish computer science. Scientometrics 97(3): 571-600 (2013) - [c58]Luis Guerra, Ruth Benavides-Piccione, Concha Bielza, Víctor Robles, Javier DeFelipe, Pedro Larrañaga:
Semi-supervised Projected Clustering for Classifying GABAergic Interneurons. AIME 2013: 156-165 - [c57]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
Learning Conditional Linear Gaussian Classifiers with Probabilistic Class Labels. CAEPIA 2013: 139-148 - [c56]Bojan Mihaljevic, Pedro Larrañaga, Concha Bielza:
Augmented Semi-naive Bayes Classifier. CAEPIA 2013: 159-167 - [c55]Pedro L. López-Cruz, Thomas D. Nielsen, Concha Bielza, Pedro Larrañaga:
Learning Mixtures of Polynomials of Conditional Densities from Data. CAEPIA 2013: 363-372 - [c54]Pedro Larrañaga, Concha Bielza:
Bayesian networks to answer challenging neuroscience questions. CBMS 2013: 2 - [c53]Laura Anton-Sanchez, Concha Bielza, Pedro Larrañaga:
Towards optimal neuronal wiring through estimation of distribution algorithms. GECCO (Companion) 2013: 1647-1650 - [i1]Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña:
Combinatorial Optimization by Learning and Simulation of Bayesian Networks. CoRR abs/1301.3871 (2013) - 2012
- [j81]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Regularized logistic regression and multiobjective variable selection for classifying MEG data. Biol. Cybern. 106(6-7): 389-405 (2012) - [j80]Rubén Armañanzas, Pedro Larrañaga, Concha Bielza:
Ensemble transcript interaction networks: A case study on Alzheimer's disease. Comput. Methods Programs Biomed. 108(1): 442-450 (2012) - [j79]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Lazy lasso for local regression. Comput. Stat. 27(3): 531-550 (2012) - [j78]Pedro Larrañaga, Hossein Karshenas, Concha Bielza, Roberto Santana:
A review on probabilistic graphical models in evolutionary computation. J. Heuristics 18(5): 795-819 (2012) - [j77]Luis Guerra, Víctor Robles, Concha Bielza, Pedro Larrañaga:
A comparison of clustering quality indices using outliers and noise. Intell. Data Anal. 16(4): 703-715 (2012) - [j76]Hanen Borchani, Concha Bielza, Pablo Martínez-Martín, Pedro Larrañaga:
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 45(6): 1175-1184 (2012) - [j75]Borja Calvo, Iñaki Inza, Pedro Larrañaga, José Antonio Lozano:
Wrapper positive Bayesian network classifiers. Knowl. Inf. Syst. 33(3): 631-654 (2012) - [j74]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Forward stagewise naïve Bayes. Prog. Artif. Intell. 1(1): 57-69 (2012) - [c52]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Maximizing the number of polychronous groups in spiking networks. GECCO (Companion) 2012: 1499-1500 - 2011
- [j73]Pedro Larrañaga, Serafín Moral:
Probabilistic graphical models in artificial intelligence. Appl. Soft Comput. 11(2): 1511-1528 (2011) - [j72]Concha Bielza, Víctor Robles, Pedro Larrañaga:
Regularized logistic regression without a penalty term: An application to cancer classification with microarray data. Expert Syst. Appl. 38(5): 5110-5118 (2011) - [j71]Endika Bengoetxea, Pedro Larrañaga, Concha Bielza, Juan A. Fernández del Pozo:
Optimal row and column ordering to improve table interpretation using estimation of distribution algorithms. J. Heuristics 17(5): 567-588 (2011) - [j70]Hanen Borchani, Pedro Larrañaga, Concha Bielza:
Classifying evolving data streams with partially labeled data. Intell. Data Anal. 15(5): 655-670 (2011) - [j69]Concha Bielza, Guangdi Li, Pedro Larrañaga:
Multi-dimensional classification with Bayesian networks. Int. J. Approx. Reason. 52(6): 705-727 (2011) - [j68]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Optimizing Brain Networks Topologies Using Multi-objective Evolutionary Computation. Neuroinformatics 9(1): 3-19 (2011) - [j67]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga, Ruth Benavides-Piccione, Javier DeFelipe:
Models and Simulation of 3D Neuronal Dendritic Trees Using Bayesian Networks. Neuroinformatics 9(4): 347-369 (2011) - [j66]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Using Bayesian networks to discover relationships between bibliometric indices. A case study of computer science and artificial intelligence journals. Scientometrics 89(2): 523-551 (2011) - [j65]Rubén Armañanzas, Yvan Saeys, Iñaki Inza, Miguel García-Torres, Concha Bielza, Yves Van de Peer, Pedro Larrañaga:
Peakbin Selection in Mass Spectrometry Data Using a Consensus Approach with Estimation of Distribution Algorithms. IEEE ACM Trans. Comput. Biol. Bioinform. 8(3): 760-774 (2011) - [c51]Pedro L. López-Cruz, Concha Bielza, Pedro Larrañaga:
The von Mises Naive Bayes Classifier for Angular Data. CAEPIA 2011: 145-154 - [c50]Hossein Karshenas, Roberto Santana, Concha Bielza, Pedro Larrañaga:
Multi-objective Optimization with Joint Probabilistic Modeling of Objectives and Variables. EMO 2011: 298-312 - [c49]Roberto Santana, Hossein Karshenas, Concha Bielza, Pedro Larrañaga:
Quantitative genetics in multi-objective optimization algorithms: from useful insights to effective methods. GECCO (Companion) 2011: 91-92 - [c48]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Affinity propagation enhanced by estimation of distribution algorithms. GECCO 2011: 331-338 - [c47]Roberto Santana, Hossein Karshenas, Concha Bielza, Pedro Larrañaga:
Regularized k-order markov models in EDAs. GECCO 2011: 593-600 - [c46]Julio H. Zaragoza, Luis Enrique Sucar, Eduardo F. Morales, Concha Bielza, Pedro Larrañaga:
Bayesian Chain Classifiers for Multidimensional Classification. IJCAI 2011: 2192-2197 - [c45]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Predicting the h-index with cost-sensitive naive Bayes. ISDA 2011: 599-604 - 2010
- [j64]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Learning Factorizations in Estimation of Distribution Algorithms Using Affinity Propagation. Evol. Comput. 18(4): 515-546 (2010) - [j63]Concha Bielza, Juan A. Fernández del Pozo, Pedro Larrañaga, Endika Bengoetxea:
Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables. Expert Syst. Appl. 37(1): 804-815 (2010) - [j62]Diego Vidaurre, Concha Bielza, Pedro Larrañaga:
Learning an L1-Regularized Gaussian Bayesian Network in the Equivalence Class Space. IEEE Trans. Syst. Man Cybern. Part B 40(5): 1231-1242 (2010) - [c44]Endika Bengoetxea, Pedro Larrañaga:
EDA-PSO: A Hybrid Paradigm Combining Estimation of Distribution Algorithms and Particle Swarm Optimization. ANTS Conference 2010: 416-423 - [c43]Alfredo Cuesta-Infante, Roberto Santana, José Ignacio Hidalgo, Concha Bielza, Pedro Larrañaga:
Bivariate empirical and n-variate Archimedean copulas in estimation of distribution algorithms. IEEE Congress on Evolutionary Computation 2010: 1-8 - [c42]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Using Probabilistic Dependencies Improves the Search of Conductance-Based Compartmental Neuron Models. EvoBIO 2010: 170-181 - [c41]Roberto Santana, Concha Bielza, Pedro Larrañaga:
Synergies between Network-Based Representation and Probabilistic Graphical Models for Classification, Inference and Optimization Problems in Neuroscience. IEA/AIE (3) 2010: 149-158 - [c40]Hanen Borchani, Pedro Larrañaga, Concha Bielza:
Mining Concept-Drifting Data Streams Containing Labeled and Unlabeled Instances. IEA/AIE (1) 2010: 531-540
2000 – 2009
- 2009
- [j61]Alfonso Ibáñez, Pedro Larrañaga, Concha Bielza:
Predicting citation count of Bioinformatics papers within four years of publication. Bioinform. 25(24): 3303-3309 (2009) - [j60]Aritz Pérez Martínez, Pedro Larrañaga, Iñaki Inza:
Bayesian classifiers based on kernel density estimation: Flexible classifiers. Int. J. Approx. Reason. 50(2): 341-362 (2009) - [j59]Txomin Romero, Pedro Larrañaga:
Triangulation of Bayesian networks with recursive estimation of distribution algorithms. Int. J. Approx. Reason. 50(3): 472-484 (2009) - [j58]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Research topics in discrete estimation of distribution algorithms based on factorizations. Memetic Comput. 1(1): 35-54 (2009) - [j57]Borja Calvo, Pedro Larrañaga, José Antonio Lozano:
Feature subset selection from positive and unlabelled examples. Pattern Recognit. Lett. 30(11): 1027-1036 (2009) - [j56]José Antonio Lozano, Qingfu Zhang, Pedro Larrañaga:
Guest Editorial: Special Issue on Evolutionary Algorithms Based on Probabilistic Models. IEEE Trans. Evol. Comput. 13(6): 1197-1198 (2009) - [j55]Rubén Armañanzas, Borja Calvo, Iñaki Inza, Marcos López-Hoyos, Víctor Martínez-Taboada, Eduardo Ucar, Irantzu Bernales, Asier Fullaondo, Pedro Larrañaga, Ana M. Zubiaga:
Microarray Analysis of Autoimmune Diseases by Machine Learning Procedures. IEEE Trans. Inf. Technol. Biomed. 13(3): 341-350 (2009) - [c39]Roberto Santana, Concha Bielza, José Antonio Lozano, Pedro Larrañaga:
Mining probabilistic models learned by EDAs in the optimization of multi-objective problems. GECCO 2009: 445-452 - [c38]Elva Díaz, Eunice Ponce de León, Pedro Larrañaga, Concha Bielza:
Probabilistic Graphical Markov Model Learning: An Adaptive Strategy. MICAI 2009: 225-236 - 2008
- [j54]Rubén Armañanzas, Iñaki Inza, Roberto Santana, Yvan Saeys, Jose Luis Flores, José Antonio Lozano, Yves Van de Peer, Rosa Blanco, Víctor Robles, Concha Bielza, Pedro Larrañaga:
A review of estimation of distribution algorithms in bioinformatics. BioData Min. 1 (2008) - [j53]Dinora Araceli Morales, Endika Bengoetxea, Pedro Larrañaga:
Selection of human embryos for transfer by Bayesian classifiers. Comput. Biol. Medicine 38(11-12): 1177-1186 (2008) - [j52]Dinora Araceli Morales, Endika Bengoetxea, Pedro Larrañaga, Miguel García, Yosu Franco, Mónica Fresnada, Marisa Merino:
Bayesian classification for the selection of in vitro human embryos using morphological and clinical data. Comput. Methods Programs Biomed. 90(2): 104-116 (2008) - [j51]Rubén Armañanzas, Iñaki Inza, Pedro Larrañaga:
Detecting reliable gene interactions by a hierarchy of Bayesian network classifiers. Comput. Methods Programs Biomed. 91(2): 110-121 (2008) - [j50]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Combining variable neighborhood search and estimation of distribution algorithms in the protein side chain placement problem. J. Heuristics 14(5): 519-547 (2008) - [j49]Guzmán Santafé, José Antonio Lozano, Pedro Larrañaga:
Inference of Population Structure Using Genetic Markers and a Bayesian Model Averaging Approach for Clustering. J. Comput. Biol. 15(2): 207-220 (2008) - [j48]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Protein Folding in Simplified Models With Estimation of Distribution Algorithms. IEEE Trans. Evol. Comput. 12(4): 418-438 (2008) - [c37]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Component weighting functions for adaptive search with EDAs. IEEE Congress on Evolutionary Computation 2008: 4066-4073 - [c36]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Adding Probabilistic Dependencies to the Search of Protein Side Chain Configurations Using EDAs. PPSN 2008: 1120-1129 - [p20]Carlos Echegoyen, Roberto Santana, José Antonio Lozano, Pedro Larrañaga:
The Impact of Exact Probabilistic Learning Algorithms in EDAs Based on Bayesian Networks. Linkage in Evolutionary Computation 2008: 109-139 - [p19]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Adaptive Estimation of Distribution Algorithms. Adaptive and Multilevel Metaheuristics 2008: 177-197 - 2007
- [j47]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Side chain placement using estimation of distribution algorithms. Artif. Intell. Medicine 39(1): 49-63 (2007) - [j46]Yvan Saeys, Iñaki Inza, Pedro Larrañaga:
A review of feature selection techniques in bioinformatics. Bioinform. 23(19): 2507-2517 (2007) - [j45]Borja Calvo, Núria López-Bigas, Simon J. Furney, Pedro Larrañaga, José Antonio Lozano:
A partially supervised classification approach to dominant and recessive human disease gene prediction. Comput. Methods Programs Biomed. 85(3): 229-237 (2007) - [j44]Teresa Miquélez, Endika Bengoetxea, Alexander Mendiburu, Pedro Larrañaga:
Combining Bayesian classifiers and estimation of distribution algorithms for optimization in continuous domains. Connect. Sci. 19(4): 297-319 (2007) - [j43]Jose Luis Flores, Iñaki Inza, Pedro Larrañaga:
Wrapper discretization by means of estimation of distribution algorithms. Intell. Data Anal. 11(5): 525-545 (2007) - [j42]Borja Calvo, Pedro Larrañaga, José Antonio Lozano:
Learning Bayesian classifiers from positive and unlabeled examples. Pattern Recognit. Lett. 28(16): 2375-2384 (2007) - [c35]Carlos Echegoyen, José Antonio Lozano, Roberto Santana, Pedro Larrañaga:
Exact Bayesian network learning in estimation of distribution algorithms. IEEE Congress on Evolutionary Computation 2007: 1051-1058 - [c34]Guzmán Santafé, José Antonio Lozano, Pedro Larrañaga:
Discriminative vs. Generative Learning of Bayesian Network Classifiers. ECSQARU 2007: 453-464 - [c33]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
The Role of a Priori Information in the Minimization of Contact Potentials by Means of Estimation of Distribution Algorithms. EvoBIO 2007: 247-257 - 2006
- [j41]Guzmán Santafé, José Antonio Lozano, Pedro Larrañaga:
Discriminative Learning of Bayesian Network Classifiers. Inteligencia Artif. 10(29): 39-47 (2006) - [j40]Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, Iñaki Inza, José Antonio Lozano, Rubén Armañanzas, Guzmán Santafé, Aritz Pérez Martínez, Victor Robles:
Machine learning in bioinformatics. Briefings Bioinform. 7(1): 86-112 (2006) - [j39]Aritz Pérez Martínez, Pedro Larrañaga, Iñaki Inza:
Supervised classification with conditional Gaussian networks: Increasing the structure complexity from naive Bayes. Int. J. Approx. Reason. 43(1): 1-25 (2006) - [j38]Guzmán Santafé, José Antonio Lozano, Pedro Larrañaga:
Bayesian Model Averaging of Naive Bayes for Clustering. IEEE Trans. Syst. Man Cybern. Part B 36(5): 1149-1161 (2006) - [c32]Aritz Pérez Martínez, Pedro Larrañaga, Iñaki Inza:
Information Theory and Classification Error in Probabilistic Classifiers. Discovery Science 2006: 347-351 - [c31]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Mixtures of Kikuchi Approximations. ECML 2006: 365-376 - [c30]Guzmán Santafé, José Antonio Lozano, Pedro Larrañaga:
Bayesian Model Averaging of TAN Models for Clustering. Probabilistic Graphical Models 2006: 271-278 - [c29]Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga:
Evolutionary Bayesian Classifier-Based Optimization in Continuous Domains. SEAL 2006: 529-536 - [p18]Victor Robles, José M. Peña, Pedro Larrañaga, María S. Pérez, Vanessa Herves:
GA-EDA: A New Hybrid Cooperative Search Evolutionary Algorithm. Towards a New Evolutionary Computation 2006: 187-219 - [p17]Teresa Miquélez, Endika Bengoetxea, Pedro Larrañaga:
Bayesian Classifiers in Optimization: An EDA-like Approach. Towards a New Evolutionary Computation 2006: 221-242 - [e2]José Antonio Lozano, Pedro Larrañaga, Iñaki Inza, Endika Bengoetxea:
Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms. Studies in Fuzziness and Soft Computing 192, Springer 2006, ISBN 978-3-540-29006-3 [contents] - 2005
- [j37]Pedro Larrañaga, José Antonio Lozano:
Editorial Introduction Special Issue on Estimation of Distribution Algorithms. Evol. Comput. 13(1) (2005) - [j36]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Globally Multimodal Problem Optimization Via an Estimation of Distribution Algorithm Based on Unsupervised Learning of Bayesian Networks. Evol. Comput. 13(1): 43-66 (2005) - [j35]Rosa Blanco, Iñaki Inza, Marisa Merino, Jorge Quiroga, Pedro Larrañaga:
Feature selection in Bayesian classifiers for the prognosis of survival of cirrhotic patients treated with TIPS. J. Biomed. Informatics 38(5): 376-388 (2005) - [j34]Pedro Larrañaga, José Antonio Lozano, José M. Peña, Iñaki Inza:
Editorial. Mach. Learn. 59(3): 211-212 (2005) - [j33]Roberto Marcondes Cesar Junior, Endika Bengoetxea, Isabelle Bloch, Pedro Larrañaga:
Inexact graph matching for model-based recognition: Evaluation and comparison of optimization algorithms. Pattern Recognit. 38(11): 2099-2113 (2005) - [c28]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Interactions and dependencies in estimation of distribution algorithms. Congress on Evolutionary Computation 2005: 1418-1425 - [c27]Guzmán Santafé, José Antonio Lozano, Pedro Larrañaga:
Discriminative Learning of Bayesian Network Classifiers via the TM Algorithm. ECSQARU 2005: 148-160 - [c26]Cristina González, A. Ramírez, José Antonio Lozano, Pedro Larrañaga:
Average Time Complexity of Estimation of Distribution Algorithms. IWANN 2005: 42-49 - [p16]Pedro Larrañaga, Iñaki Inza, Jose Luis Flores:
A Guide to the Literature on Inferring Genetic Networks by Probabilistic Graphical Models. Data Analysis and Visualization in Genomics and Proteomics 2005: 215-238 - 2004
- [j32]Iñaki Inza, Pedro Larrañaga, Rosa Blanco, Antonio J. Cerrolaza:
Filter versus wrapper gene selection approaches in DNA microarray domains. Artif. Intell. Medicine 31(2): 91-103 (2004) - [j31]Víctor Robles, Pedro Larrañaga, José M. Peña, Ernestina Menasalvas Ruiz, María S. Pérez, Vanessa Herves, Anita Wasilewska:
Bayesian network multi-classifiers for protein secondary structure prediction. Artif. Intell. Medicine 31(2): 117-136 (2004) - [j30]Txomin Romero, Pedro Larrañaga, Basilio Sierra:
Learning Bayesian Networks In The Space Of Orderings With Estimation Of Distribution Algorithms. Int. J. Pattern Recognit. Artif. Intell. 18(4): 607-625 (2004) - [j29]Rosa Blanco, Pedro Larrañaga, Iñaki Inza, Basilio Sierra:
Gene Selection For Cancer Classification Using Wrapper Approaches. Int. J. Pattern Recognit. Artif. Intell. 18(8): 1373-1390 (2004) - [j28]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Unsupervised Learning Of Bayesian Networks Via Estimation Of Distribution Algorithms: An Application To Gene Expression Data Clustering. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 12(Supplement-1): 63-82 (2004) - [c25]José M. Peña, Víctor Robles, Pedro Larrañaga, Vanessa Herves, Francisco Rosales, María S. Pérez:
GA-EDA: Hybrid Evolutionary Algorithm Using Genetic and Estimation of Distribution Algorithms. IEA/AIE 2004: 361-371 - [c24]Rosa Blanco, Linda C. van der Gaag, Iñaki Inza, Pedro Larrañaga:
Selective Classifiers Can Be Too Restrictive: A Case-Study in Oesophageal Cancer. ISBMDA 2004: 212-223 - [c23]Roberto Santana, Pedro Larrañaga, José Antonio Lozano:
Protein Folding in 2-Dimensional Lattices with Estimation of Distribution Algorithms. ISBMDA 2004: 388-398 - 2003
- [j27]Pedro Larrañaga, José Antonio Lozano, Heinz Mühlenbein:
Algoritmos de Estimación de Distribuciones en Problemas de Optimización Combinatoria. Inteligencia Artif. 7(19): 149-168 (2003) - [j26]Rosa Blanco, Iñaki Inza, Pedro Larrañaga:
Learning Bayesian networks in the space of structures by estimation of distribution algorithms. Int. J. Intell. Syst. 18(2): 205-220 (2003) - [c22]Víctor Robles, Pedro Larrañaga, José M. Peña, Oscar Marbán, F. Javier Crespo, María S. Pérez:
Collaborative Filtering Using Interval Estimation Naïve Bayes. AWIC 2003: 46-53 - [c21]Víctor Robles, Pedro Larrañaga, José M. Peña, María S. Pérez, Ernestina Menasalvas Ruiz, Vanessa Herves:
Learning Semi Naïve Bayes Structures by Estimation of Distribution Algorithms. EPIA 2003: 244-258 - [c20]Víctor Robles, Pedro Larrañaga, José M. Peña, Ernestina Menasalvas Ruiz, María S. Pérez:
Interval Estimation Naïve Bayes. IDA 2003: 143-154 - [c19]Cristina González, Juan Diego Rodríguez, José Antonio Lozano, Pedro Larrañaga:
Analysis of the Univariate Marginal Distribution Algorithm Modeled by Markov Chains. IWANN (1) 2003: 510-517 - [c18]Víctor Robles, María S. Pérez, Vanessa Herves, José M. Peña, Pedro Larrañaga:
Parallel Stochastic Search for Protein Secondary Structure Prediction. PPAM 2003: 1162-1169 - [c17]Víctor Robles, Pedro Larrañaga, Ernestina Menasalvas Ruiz, María S. Pérez, Vanessa Herves:
Improvement of Naïve Bayes Collaborative Filtering Using Interval Estimation. Web Intelligence 2003: 168-174 - 2002
- [j25]Pedro Larrañaga, José Antonio Lozano:
Synergies between evolutionary computation and probabilistic graphical models. Int. J. Approx. Reason. 31(3): 155-156 (2002) - [j24]Cristina González, José Antonio Lozano, Pedro Larrañaga:
Mathematical modelling of UMDAc algorithm with tournament selection. Behaviour on linear and quadratic functions. Int. J. Approx. Reason. 31(3): 313-340 (2002) - [j23]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Learning Recursive Bayesian Multinets for Data Clustering by Means of Constructive Induction. Mach. Learn. 47(1): 63-89 (2002) - [j22]Endika Bengoetxea, Pedro Larrañaga, Isabelle Bloch, Aymeric Perchant, Claudia Boeres:
Inexact graph matching by means of estimation of distribution algorithms. Pattern Recognit. 35(12): 2867-2880 (2002) - [c16]Rosa Blanco, Iñaki Inza, Pedro Larrañaga:
Floating Search Methods in Learning Bayesian Networks. Probabilistic Graphical Models 2002 - [c15]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Unsupervised Learning of Bayesian Networks Via Estimation of Distribution Algorithms. Probabilistic Graphical Models 2002 - [p15]Pedro Larrañaga:
An Introduction to Probabilistic Graphical Models. Estimation of Distribution Algorithms 2002: 27-56 - [p14]Pedro Larrañaga:
A Review on Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 57-100 - [p13]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Benefits of Data Clustering in Multimodal Function Optimization via EDAs. Estimation of Distribution Algorithms 2002: 101-127 - [p12]José Antonio Lozano, Ramón Sagarna, Pedro Larrañaga:
Parallel Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 129-145 - [p11]Cristina González, José Antonio Lozano, Pedro Larrañaga:
Mathematical Modeling of Discrete Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 147-163 - [p10]Endika Bengoetxea, Teresa Miquélez, Pedro Larrañaga, José Antonio Lozano:
Experimental Results in Function Optimization with EDAs in Continuous Domain. Estimation of Distribution Algorithms 2002: 181-194 - [p9]Ramón Sagarna, Pedro Larrañaga:
Solving the 0-1 Knapsack Problem with EDAs. Estimation of Distribution Algorithms 2002: 195-209 - [p8]Víctor Robles, P. de Miguel, Pedro Larrañaga:
Solving the Traveling Salesman Problem with EDAs. Estimation of Distribution Algorithms 2002: 211-229 - [p7]Endika Bengoetxea, Pedro Larrañaga, Aymeric Perchant, Isabelle Bloch:
Solving Graph Matching with EDAs Using a Permutation-Based Representation. Estimation of Distribution Algorithms 2002: 243-265 - [p6]Iñaki Inza, Pedro Larrañaga, Basilio Sierra:
Feature Subset Selection by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 269-293 - [p5]Iñaki Inza, Pedro Larrañaga, Basilio Sierra:
Feature Weighting for Nearest Neighbor by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 295-311 - [p4]Basilio Sierra, E. A. Jiménez, Iñaki Inza, Pedro Larrañaga, J. Muruzábal:
Rule Induction by Estimation of Distribution Algorithms. Estimation of Distribution Algorithms 2002: 313-322 - [p3]L. M. Campos, José A. Gámez, Pedro Larrañaga, Serafín Moral, Txomin Romero:
Partial Abductive Inference in Bayesian Networks: An Empirical Comparison Between GAs and EDAs. Estimation of Distribution Algorithms 2002: 323-341 - [p2]Josep Roure, Pedro Larrañaga, Ramon Sangüesa:
An Empirical Comparison Between K-Means, GAs and EDAs in Partitional Clustering. Estimation of Distribution Algorithms 2002: 343-360 - [p1]Carlos Cotta, Enrique Alba, Ramón Sagarna, Pedro Larrañaga:
Adjusting Weights in Artificial Neural Networks using Evolutionary Algorithms. Estimation of Distribution Algorithms 2002: 361-377 - [e1]Pedro Larrañaga, José Antonio Lozano:
Estimation of Distribution Algorithms. Genetic Algorithms and Evolutionary Computation, Springer 2002, ISBN 978-1-4613-5604-2 [contents] - 2001
- [j21]Basilio Sierra, Nicolás Serrano, Pedro Larrañaga, Eliseo J. Plasencia, Iñaki Inza, Juan José Jiménez, Pedro Revuelta, María Luisa Mora:
Using Bayesian networks in the construction of a bi-level multi-classifier. A case study using intensive care unit patients data. Artif. Intell. Medicine 22(3): 233-248 (2001) - [j20]Iñaki Inza, Marisa Merino, Pedro Larrañaga, Jorge Quiroga, Basilio Sierra, Marcos Girala:
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 23(2): 187-205 (2001) - [j19]Iñaki Inza, Pedro Larrañaga, Basilio Sierra:
Feature subset selection by Bayesian networks: a comparison with genetic and sequential algorithms. Int. J. Approx. Reason. 27(2): 143-164 (2001) - [j18]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Performance evaluation of compromise conditional Gaussian networks for data clustering. Int. J. Approx. Reason. 28(1): 23-50 (2001) - [j17]José M. Peña, José Antonio Lozano, Pedro Larrañaga, Iñaki Inza:
Dimensionality Reduction in Unsupervised Learning of Conditional Gaussian Networks. IEEE Trans. Pattern Anal. Mach. Intell. 23(6): 590-603 (2001) - [c14]Basilio Sierra, Elena Lazkano, Iñaki Inza, Marisa Merino, Pedro Larrañaga, Jorge Quiroga:
Prototype Selection and Feature Subset Selection by Estimation of Distribution Algorithms. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS. AIME 2001: 20-29 - [c13]José M. Peña, I. Izarzugaza, José Antonio Lozano, E. Aldasoro, Pedro Larrañaga:
Geographical clustering of cancer incidence by means of Bayesian networks and conditional Gaussian networks. AISTATS 2001: 237-242 - [c12]Endika Bengoetxea, Pedro Larrañaga, Isabelle Bloch, Aymeric Perchant:
Estimation of Distribution Algorithms: A New Evolutionary Computation Approach for Graph Matching Problems. EMMCVPR 2001: 454-468 - [c11]Basilio Sierra, Iñaki Inza, Pedro Larrañaga:
On Applying Supervised Classification Techniques in Medicine. ISMDA 2001: 14-19 - 2000
- [j16]Iñaki Inza, Pedro Larrañaga, Ramon Etxeberria, Basilio Sierra:
Feature Subset Selection by Bayesian network-based optimization. Artif. Intell. 123(1-2): 157-184 (2000) - [j15]Cristina González, José Antonio Lozano, Pedro Larrañaga:
Analyzing the Population Based Incremental Learning Algorithm by Means of Discrete Dynamical Systems. Complex Syst. 12(4) (2000) - [j14]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
An improved Bayesian structural EM algorithm for learning Bayesian networks for clustering. Pattern Recognit. Lett. 21(8): 779-786 (2000) - [c10]Basilio Sierra, Iñaki Inza, Pedro Larrañaga:
Medical Bayes Networks. ISMDA 2000: 4-14 - [c9]Iñaki Inza, Marisa Merino, Pedro Larrañaga, Jorge Quiroga, Basilio Sierra, Marcos Girala:
Feature Subset Selection Using Probabilistic Tree Structures. A Case Study in the Survival of Cirrhotic Patients Treated with TIPS. ISMDA 2000: 97-110 - [c8]Pedro Larrañaga, Ramon Etxeberria, José Antonio Lozano, José M. Peña:
Combinatonal Optimization by Learning and Simulation of Bayesian Networks. UAI 2000: 343-352
1990 – 1999
- 1999
- [j13]Pedro Larrañaga, Cindy M. H. Kuijpers, Roberto H. Murga, Iñaki Inza, S. Dizdarevic:
Genetic Algorithms for the Travelling Salesman Problem: A Review of Representations and Operators. Artif. Intell. Rev. 13(2): 129-170 (1999) - [j12]José Antonio Lozano, Pedro Larrañaga:
Applying genetic algorithms to search for the best hierarchical clustering of a dataset. Pattern Recognit. Lett. 20(9): 911-918 (1999) - [j11]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
An empirical comparison of four initialization methods for the K-Means algorithm. Pattern Recognit. Lett. 20(10): 1027-1040 (1999) - [j10]Iñaki Inza, Pedro Larrañaga, Basilio Sierra, Ramon Etxeberria, José Antonio Lozano, José M. Peña:
Representing the behaviour of supervised classification learning algorithms by Bayesian networks. Pattern Recognit. Lett. 20(11-13): 1201-1209 (1999) - [j9]José M. Peña, José Antonio Lozano, Pedro Larrañaga:
Learning Bayesian networks for clustering by means of constructive induction. Pattern Recognit. Lett. 20(11-13): 1219-1230 (1999) - [j8]José Antonio Lozano, Pedro Larrañaga, Manuel Graña, F. Xabier Albizuri:
Genetic Algorithms: Bridging the Convergence Gap. Theor. Comput. Sci. 229(1): 11-22 (1999) - [c7]Basilio Sierra, Nicolás Serrano, Pedro Larrañaga, Eliseo J. Plasencia, Iñaki Inza, Juan José Jiménez, Jose María De la Rosa, María Luisa Mora:
Machine Learning Inspired Approaches to Combine Standard Medical Measures at an Intensive Care Unit. AIMDM 1999: 366-371 - 1998
- [j7]José Antonio Lozano, Pedro Larrañaga:
Aplicación de los algoritmos genéticos al problema del clustering jerárquico. Inteligencia Artif. 2(5): 62-67 (1998) - [j6]Basilio Sierra, Pedro Larrañaga:
Predicting survival in malignant skin melanoma using Bayesian networks automatically induced by genetic algorithms. An empirical comparison between different approaches. Artif. Intell. Medicine 14(1-2): 215-230 (1998) - 1997
- [j5]Ramon Etxeberria, Pedro Larrañaga, Juan M. Picaza:
Analysis of the behaviour of genetic algorithms when learning Bayesian network structure from data. Pattern Recognit. Lett. 18(11-13): 1269-1273 (1997) - [j4]Pedro Larrañaga, Cindy M. H. Kuijpers, Mikel Poza, Roberto H. Murga:
Decomposing Bayesian networks: triangulation of the moral graph with genetic algorithms. Stat. Comput. 7(1): 19-34 (1997) - [j3]F. Xabier Albizuri, Alicia D'Anjou, Manuel Graña, Pedro Larrañaga:
Structure of the high-order Boltzmann machine from independence maps. IEEE Trans. Neural Networks 8(6): 1351-1358 (1997) - [c6]Pedro Larrañaga, Basilio Sierra, Miren J. Gallego, Maria J. Michelena, Juan M. Picaza:
Learning Bayesisan Networks by Genetic Algorithms: A Case Study in the Prediction of Survival in Malignant Skin Melanoma. AIME 1997: 261-272 - [c5]Pedro Larrañaga, Miren J. Gallego, Basilio Sierra, L. Urkola, Maria J. Michelena:
Bayesian Networks, Rule Induction and Logistic Regression in the Prediction of the Survival of Women Suffering from Breast Cancer. EPIA 1997: 303-308 - [c4]Ana Isabel González, Manuel Graña, José Antonio Lozano, Pedro Larrañaga:
Experimental Results of a Michigan-like Evolution Strategy for Non-stationary Clustering. ICANNGA 1997: 555-559 - 1996
- [j2]Pedro Larrañaga, Mikel Poza, Yosu Yurramendi, Roberto H. Murga, Cindy M. H. Kuijpers:
Structure Learning of Bayesian Networks by Genetic Algorithms: A Performance Analysis of Control Parameters. IEEE Trans. Pattern Anal. Mach. Intell. 18(9): 912-926 (1996) - [j1]Pedro Larrañaga, Cindy M. H. Kuijpers, Roberto H. Murga, Yosu Yurramendi:
Learning Bayesian network structures by searching for the best ordering with genetic algorithms. IEEE Trans. Syst. Man Cybern. Part A 26(4): 487-493 (1996) - 1995
- [c3]Pedro Larrañaga, Roberto H. Murga, Mikel Poza, Cindy M. H. Kuijpers:
Structure Learning of Bayesian Networks by Hybrid Genetic Algorithms. AISTATS 1995: 165-174 - 1993
- [c2]Pedro Larrañaga, Manuel Graña, Alicia D'Anjou, Francisco Javier Torrealdea:
Genetic Algorithms Elitist Probabilistic of Degree 1, a generalization of Simulated Annealing. AI*IA 1993: 208-217 - [c1]Pedro Larrañaga, Yosu Yurramendi:
Structure learning approaches in Causal Probalistics Networks. ECSQARU 1993: 227-232
Coauthor Index
Concha Bielza
aka: Concepcion Bielza
aka: Concepcion Bielza
[j160] [j159] [j158] [j157] [j156] [j155] [j154] [j153] [j152] [j151] [j150] [c86] [c85] [c84] [i11] [i10] [j149] [j148] [j147] [j146] [j145] [j144] [j143] [c83] [c82] [c81] [c80] [d1] [i9] [j142] [j141] [j140] [j139] [j138] [j137] [c79] [c78] [c77] [i8] [j136] [j135] [j134] [j133] [c76] [i7] [i6] [j132] [j131] [j130] [j129] [j128] [j127] [j126] [j125] [j124] [j123] [j122] [j121] [c75] [c74] [c73] [c72] [c71] [c70] [c69] [i5] [i4] [i3] [j120] [j119] [j118] [c68] [j117] [j116] [j115] [j114] [j113] [c67] [c66] [c65] [c64] [c63] [i2] [j112] [j111] [j110] [j109] [j108] [j107] [j106] [c62] [c61] [c60] [j105] [j104] [j103] [j102] [j101] [j100] [j99] [j98] [j97] [j96] [c59] [j95] [j94] [j93] [j91] [j90] [j89] [j88] [j87] [j86] [j85] [j84] [j83] [j82] [c58] [c57] [c56] [c55] [c54] [c53] [j81] [j80] [j79] [j78] [j77] [j76] [j74] [c52] [j72] [j71] [j70] [j69] [j68] [j67] [j66] [j65] [c51] [c50] [c49] [c48] [c47] [c46] [c45] [j63] [j62] [c43] [c42] [c41] [c40] [j61] [c39] [c38] [j54] [j40]
[i1] [j75] [j64] [j58] [j57] [j56] [c39] [j54] [j50] [j49] [j48] [c37] [c36] [p20] [p19] [j47] [j45] [j42] [c35] [c34] [c33] [j41] [j40] [j38] [c31] [c30] [e2] [j37] [j36] [j34] [c28] [c27] [c26] [j28] [c23] [j27] [c19] [j25] [j24] [j23] [c15] [p13] [p12] [p11] [p10] [e1] [j18] [j17] [c13] [j15] [j14] [c8] [j12] [j11] [j10] [j9] [j8] [j7] [c4]
Víctor Robles
aka: Victor Robles
aka: Victor Robles
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