Publications


On this page you will find all publications made in the context of the PGMs4SDA research project.

Reference information on all PGMs4SDA publications can be found on CiteULike under tag "".

2017

  1. Juan A. Aledo, José A. Gámez, David Molina (2017) Tackling the supervised label ranking problem by bagging weak learners. Information Fusion 35, 38-50. May 2017. DOI: 10.1016/j.inffus.2016.09.002
  2. Jacinto Arias, José A. Gámez, José Miguel Puerta (2017) Learning distributed discrete Bayesian Network Classifiers under MapReduce with Apache Spark. Knowledge-Based Systems 117, 16-26. February 2017. DOI: 10.1016/j.knosys.2016.06.013.
  3. Antonio Jesús Díaz-Honrubia, Johan De Praeter, Glenn Van Wallendael, J. L. Martinez, Pedro Cuenca, José Miguel Puerta, José A. Gámez (2017) CTU splitting algorithm for H.264/AVC and HEVC simultaneous encoding. The Journal of Supercomputing 73(1), 190-202. January 2017  DOI: 10.1007/s11227-016-1683-1
  4. Darío Ramos-López, Andrés R. Masegosa, Ana M. Martínez, Antonio Salmerón, Thomas D. Nielsen, Helge Langseth, Anders L. Madsen (2017) MAP inference in dynamic hybrid Bayesian networks. Progress in Artificial Intelligence, In press. 27 January 2017. DOI: 10.1007/s13748-017-0115-7 
  5. Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo (2017). Evaluating Interval-Valued Influence Diagrams. International Journal of Approximate Reasoning 80, 393-411. January 2017. DOI: 10.1016/j.ijar.2016.05.004

2016

  1. Fernando Rubio, Jesus Martínez-Gómez, M. Julia Flores, José Miguel Puerta (2016) Comparison between Bayesian network classifiers and SVMs for semantic localization. Expert Systems with Applications 64, 434-443. 01 December 2016. DOI: 10.1016/j.eswa.2016.08.029
  2. Juan A. Aledo, José A. Gámez, David Molina, Alejandro Rosete (2016) FSS-OBOP: Feature subset selection guided by a bucket order consensus ranking. Proceedings of IEEE Symposium Series on Computational Intelligence (SSCI-CIDM 2016), 1-8, IEEE Press. December 2016. DOI: 10.1109/SSCI.2016.7849924
  3. Pablo Bermejo, José A. Gámez, José Miguel Puerta, Marco A. Esquivias, Pedro J. Tárraga (2016) Construction of a Semi-Naive Model to Predict Early Readmission of COPD Patients by Using Quality Care Information. Proceedings of ICDM Workshop on Data Mining in Biomedical Informatics and Healthcare, 233-240, IEEE Press. December 2016. DOI: 10.1109/ICDMW.2016.0040
  4. M.D. Sánchez-García, J. del Sagrado, A. Salmerón, R. Rumí (2016). PGMs4SDA: a public repository for Probabilistic Graphical Models. Procs of the 28th Benelux Conference On Artificial Intelligence (BNAIC'16), pp. 241-242. November 2016.
  5. Jacinto Arias, Jesus Martínez-Gómez, José A. Gámez, Alba Garcia Seco de Herrera, Henning Müller (2016) Medical image modality classification using discrete Bayesian networks. Computer Vision and Image Understanding 151, 61-71. October 2016. DOI: 10.1016/j.cviu.2016.04.002
  6. Julia Flores, Ann E. Nicholson, Rosa F. Ropero (2016) Dynamic OOBNs applied to water management in dams. Proceedings of the Int. Conference on Knowledge Engineering and Applications (ICKEA 2016), 1-8, IEEE Press.  September 2016. DOI: 10.1109/ICKEA.2016.7803030
  7. Antonio Jesús Díaz-Honrubia, Gabriel Cebrián-Márquez, José Luis Martínez, Pedro Cuenca, José Miguel Puerta, José Antonio Gámez (2016) Low-complexity heterogeneous architecture for H.264/HEVC video transcoding. Journal of Real-Time Image Processing 12(2), 311-327. August 2016. DOI: 10.1007/s11554-014-0477-z
  8. Byron Oviedo, Serafín Moral, Amilkar Puris (2016). A Hierarchical Clustering Method: Applications to Educational Data. Intelligent Data Analysis 20, 933-951. June 2016. DOI: 10.3233/IDA-160839
  9. Carlos Morales, Serafín Moral (2016). Modeling aircrew information management for estimation of situational awareness using dynamic Bayesian networks. Simulation Modelling Practice and Theory 65, 93–103. June 2016. DOI: 10.1016/j.simpat.2015.11.008
  10. Carlos Morales, Serafín Moral (2016). Regression Methods Applied to Flight Variables for Situational Awareness Estimation Using Dynamic Bayesian Networks. Proceedings of the Eighth International Conference on Probabilistic Graphical Models, 356–367.
  11. Ana D. Maldonado, Pedro A. Aguilera, Antonio Salmerón (2016) Modeling zero-inflated explanatory variables in hybrid Bayesian network classifiers for species occurrence prediction. Environmental Modelling & Software 82, 31-43. 01 April 2016. DOI: 10.1016/j.envsoft.2016.04.003. 
  12. Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen (2016). Improvements to Variable Elimination and Symbolic Probabilistic Inference for Evaluating Influence Diagrams. International Journal of Approximate Reasoning 70, 13-35. March 2016. DOI: 10.1016/j.ijar.2015.11.011
  13. Andrés R. Masegosa, Ad J. Feelders, Linda C. van der Gaag (2016). Learning from incomplete data in Bayesian networks with qualitative influences. International Journal of Approximate Reasoning 69, 18-34. February 2016. DOI: 10.1016/j.ijar.2015.11.004
  14. Rafael Cabañas, Manuel Gómez-Olmedo, Andrés Cano (2016). Using Binary Trees for the Evaluation of Influence Diagrams. International Journal of Uncertainty, Fuzziness and Knowlegde-Based Systems 24 Issue 1, 59-89. February 2016. DOI: 10.1142/S0218488516500045
  15. Antonio Jesús Díaz-Honrubia, José Luis Martínez, Pedro Cuenca, José Antonio Gámez, José Miguel Puerta (2016) Adaptive Fast Quadtree Level Decision Algorithm for H.264 to HEVC Video Transcoding. IEEE Transactions on Circuits Systems and Video Technology 26(1), 154-168. January 2016. DOI: 10.1109/TCSVT.2015.2473299
  16. Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta (2016) A scalable pairwise class interaction framework for multidimensional classification. International Journal of Approximate Reasoning 68, 194-210. January 2016. DOI: 10.1016/j.ijar.2015.07.007

2015

  1. Ana D. Maldonado, Rosa F. Ropero, Pedro A. Aguilera, Rafael Rumí, Antonio Salmerón (2015) Continuous Bayesian networks for the estimation of species richness. Progress in Artificial Intelligence 4, 49-57. 07 December 2015. DOI:10.1007/s13748-015-0067-8. Online access.
  2. Jacinto Arias, José A. Gámez, José Miguel Puerta (2015) Scalable Learning of k-dependence Bayesian Classifiers under MapReduce. Proceedings of the Ninth IEEE International Conference on Big Data Science and Engineering,
    (TrustCom/BigDataSE/ISPA), Volume 2, 25-32. 03 December 2015. DOI: 10.1109/Trustcom.2015.558
  3. Ana D. Maldonado, Rosa F. Ropero, Pedro A. Aguilera, Antonio Fernández, Rafael Rumí, Antonio Salmerón (2015) Continuous Bayesian networks vs. other methods for regression in environmental modelling. Procedia Environmental Sciences 26, 70-73. DOI: 10.1016/j.proenv.2015.05.027. Online access.
  4. Ana D. Maldonado, Rosa F. Ropero, Pedro Aguilera, Rafael Rumí, Antonio Salmerón (2015) Estimation of species richness Using Bayesian networks. CAEPIA'2015. Lecture Notes in Artificial Intelligence 9422, 153-163. 14 November 2015. DOI: 0.1007/978-3-319-24598-0_14. Online access.
  5. Ana D. Maldonado, Pedro A. Aguilera, Antonio Salmerón (2015) Continuous Bayesian networks for probabilistic environmental risk mapping. Stochastic Environmental Research and Risk Assessment. 31 July 2015. DOI: 0.1007/s00477-015-1133-2. Online access.
  6. Inmaculada Pérez-Bernabé, Antonio Fernández, Rafael Rumí, Antonio Salmerón (2015) Parameter learning in hybrid Bayesian networks using prior knowledge. Data Mining and Knowledge Discovery. Available on-line. 14 July 2015. DOI: 10.1007/s10618-015-0429-7. Online access.
  7. Byron Oviedo, Luis Moreira, Amilkar Puris, Serafín Moral (2015). Learning Bayesian network by a Mesh of Points. APCASE '15 Proceedings of the 2015 Asia-Pacific Conference on Computer Aided System Engineering, IEEE Computer Society, 163-168.DOI: 10.1109/APCASE.2015.36
  8. Byron Oviedo, Luis Moreira, Amilkar Puris, Pavel Novoa, Serafín Moral (2016). Learning Bayesian network by a Mesh of Points. Proceedings IEEE Congress on Evolutionary Computation (CEC), 3983-3989.DOI: 10.1109/CEC.2016.7744295
  9. Juan Ignacio Alonso-Barba, Luis de la Ossa, Olivier Regnier-Coudert, John A. W. McCall, José A. Gámez, José Miguel Puerta (2015) Ant Colony and Surrogate Tree-Structured Models for Orderings-Based Bayesian Network Learning. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015), 543-550. ACM Press.
    DOI: 10.1145/2739480.2754806
  10. Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo (2015). Similarity Measures for Building Binary Utility Trees in the Approximate Evaluation of Influence Diagrams. Actas de la XVI Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA'15), 21-30.
  11. Inmaculada Pérez-Bernabé, Antonio Salmerón, Helge Langseth (2015) Learning Conditional Distributions Using Mixtures of Truncated Basis Functions. ECSQARU'2015. Lecture Notes in Artificial Intelligence 9161, 397-406. 12 July 2015. DOI:10.1007/978-3-319-20807-7_36. Online access.
  12. Andrés Cano, Manuel Gómez-Olmedo, Cora B. Pérez-Ariza (2015). An Extended Approach to Learning Recursive Probability Trees from Data. International Journal of Intelligent Systems, 30, 355-383. DOI: 10.1002/int.21703
  13. Antonio Jesús Díaz-Honrubia, José Luis Martínez, Pedro Cuenca, José Antonio Gámez, José Miguel Puerta (2015) A Data-Driven Probabilistic CTU Splitting Algorithm for Fast H.264/HEVC Video Transcoding. Proceedings of Data Compression Conference, DCC 2015, 449. July 2015. DOI: 10.1109/DCC.2015.46
  14. Rafael Cabañas, Alessandro Antonucci, Andrés Cano, Manuel Gómez-Olmedo (2015). Variable Elimination for Interval-Valued Influence Diagrams. Proceedings of ECSQUARU 2015, LNAI 9161, Springer, 541-551. July 2015. DOI: 10.1007/978-3-319-20807-7_49
  15. Carlos Morales, Serafín Moral (2015). Discretization of simulated flight parameters for estimation of Situational Awareness using Dynamic Bayesian Networks. Proceedings of the 2015 IEEE Twelfth International Symposium on Autonomous Decentralized Systems (ISADS), 196-201
  16. Barry R. Cobb, Alan W. Johnson, Rafael Rumí, Antonio Salmerón (2015) Accurate lead time demand modeling and optimal inventory policies in continuous review systems. International Journal of Production Economics 163, 124-136. May 2015. DOI:10.1016/j.ijpe.2015.02.017. Online access.
  17. I.M. Águila, J. del Sagrado (2015).  Bayesian networks for enhancement of requirements engineering: a literature review. Requirements Engineering, online: 08 May 2015, DOI:10.1007/s00766-015-0225-3 Online access
  18. Andrés R. Masegosa, Rubén Armañanzas, María M. Abad-Grau, Víctor Potenciano, Serafín Moral, Pedro Larrañaga, Concha Bielza, Fuencisla Matesanz (2015). Discretization of Expression Quantitative Trait Loci in Association Analysis Between Genotypes and Expression Data. Current Bioinformatics, 10 (2), 144-164. May 2015. DOI: 10.2174/157489361002150518123918
  19. Sara Sáez-Atienzar, Jesus Martínez-Gómez, Juan Ignacio Alonso-Barba, José Miguel Puerta, María F. Galindo, Joaquín Jordán, Luis de la Ossa (2015) Automatic quantification of the subcellular localization of chimeric GFP protein supported by a two-level Naive Bayes classifier. Expert Systems with Applications 42(3), 1531-1537. 15 February 2015. DOI: 10.1016/j.eswa.2014.09.052
  20. Jacinto Arias, José A. Gámez, José Miguel Puerta (2015) Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy. International Journal of Intelligent Systems 30(3), 292-325. DOI:10.1002/int.21701
  21. M. Julia Flores, José A. Gámez (2015) Impact on Bayesian Networks Classifiers When Learning from Imbalanced Datasets. Proceedings of the International Conference on Agents and Artificial Intelligence (ICAART), Volume 2, 382-389.
  22. J. M. Peña, M. Gómez-Olmedo (2016). Learning Marginal AMP Chain Graphs under Faithfulness Revisited. International Journal of Approximate Reasoning 68, 108-126. January 2016. DOI: 10.1016/j.ijar.2015.09.004

2014

  1. D. Sonntag, J. M. Peña, M. Gómez-Olmedo. Approximate Counting of Graphical Models Via MCMC Revisited. International Journal of Intelligent Systems 30 (3), 384–420. December 2014. DOI: 10.1002/int.21704
  2. Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta (2014) A Pairwise Class Interaction Framework for Multilabel Classification. Proceedings of the 7th European Workshop on Probabilistic Graphical Models (PGM 2014), LNCS 8754, 17-32. DOI: 10.1007/978-3-319-11433-0_2
  3. A. Cano, M. Gómez-Olmedo, S. Moral, C.B. Pérez-Ariza (2014). Extended Probability Trees for Probabilistic Graphical Models. PGM 2014, LNAI 8754, 113-128. DOI: 10.1007/978-3-319-11433-0_8
  4. Antonio Jesús Díaz-Honrubia, José Luis Martínez, José Miguel Puerta, José A. Gámez, Jan De Cock, Pedro Cuenca (2014) Fast quadtree level decision algorithm for H.264/HEVC transcoder. Proceedings of the IEEE International Conference on Image Processing (ICIP 2014), 2497-2501. DOI: 10.1109/ICIP.2014.7025505
  5. Rafael Cabañas, Andrés Cano, Manuel Gómez-Olmedo, Anders L. Madsen (2014). On SPI-Lazy evaluation of influence diagrams. PGM 2014, LNAI 8754, 97-112. DOI: 10.1007/978-3-319-11433-0_7