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ORCIDJosé A. Gámez 0001
José Antonio Gámez
Person information
- affiliation: University of Castilla-La Mancha, Spain
Other persons with the same name
- José A. Gámez 0002 — RWTH Aachen University, Germany
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2020 – today
- 2023
[j72]Juan C. Alfaro
, Juan A. Aledo, José A. Gámez:
Pairwise learning for the partial label ranking problem. Pattern Recognit. 140: 109590 (2023)- 2022
- [j71]Guillermo Fernández, Juan A. Aledo, José Antonio Gámez, José Miguel Puerta:
Factual and Counterfactual Explanations in Fuzzy Classification Trees. IEEE Trans. Fuzzy Syst. 30(12): 5484-5495 (2022) - [c75]Víctor Pérez-Piqueras, Pablo Bermejo López, José A. Gámez:
GRASP-Based Hybrid Search to Solve the Multi-objective Requirements Selection Problem. OLA 2022: 189-200 - [c74]Juan C. Alfaro, Juan A. Aledo, José A. Gámez:
Integrating Bayesian network classifiers to deal with the partial label ranking problem. PGM 2022: 337-348 - [c73]Víctor Pérez-Piqueras, Pablo Bermejo López, José A. Gámez:
Estimation of Distribution Algorithms Applied to the Next Release Problem. SOCO 2022: 98-108 - 2021
- [j70]Annalisa Appice, Sergio Escalera, José A. Gámez, Heike Trautmann:
Introduction to the special issue of the ECML PKDD 2021 journal track. Data Min. Knowl. Discov. 35(6): 2540-2541 (2021) - [j69]Enrique González Rodrigo, Juan C. Alfaro, Juan A. Aledo, José A. Gámez:
Mixture-Based Probabilistic Graphical Models for the Label Ranking Problem. Entropy 23(4): 420 (2021) - [j68]Juan C. Alfaro, Juan A. Aledo, José A. Gámez:
Learning decision trees for the partial label ranking problem. Int. J. Intell. Syst. 36(2): 890-918 (2021) - [j67]José Miguel Puerta, Juan A. Aledo, José A. Gámez, Jorge D. Laborda:
Efficient and accurate structural fusion of Bayesian networks. Inf. Fusion 66: 155-169 (2021) - [j66]Juan A. Aledo, José A. Gámez, Alejandro Rosete:
A highly scalable algorithm for weak rankings aggregation. Inf. Sci. 570: 144-171 (2021) - [j65]Annalisa Appice, Sergio Escalera, José A. Gámez, Heike Trautmann:
Introduction to the special issue of the ECML PKDD 2021 journal track. Mach. Learn. 110(10): 2991-2992 (2021) - [c72]Ricardo Montañana, José A. Gámez, José Miguel Puerta:
STree: A Single Multi-class Oblique Decision Tree Based on Support Vector Machines. CAEPIA 2021: 54-64 - [c71]Juan C. Alfaro, Juan A. Aledo, José A. Gámez:
Mixture-Based Probabilistic Graphical Models for the Partial Label Ranking Problem. IDEAL 2021: 277-288 - [c70]Alejandro Zornoza Martínez, Jesus Martínez-Gómez, José A. Gámez:
A Data-Driven Approach for Components Useful Life Estimation in Wind Turbines. SOCO 2021: 37-47 - 2020
- [j64]F. Javier Ramírez, Juan A. Aledo, José A. Gámez, Duc Truong Pham:
Economic modelling of robotic disassembly in end-of-life product recovery for remanufacturing. Comput. Ind. Eng. 142: 106339 (2020) - [c69]Juan C. Alfaro, Juan A. Aledo, José A. Gámez:
Averaging-Based Ensemble Methods for the Partial Label Ranking Problem. HAIS 2020: 410-423
2010 – 2019
- 2019
- [j63]Juan A. Aledo, José A. Gámez, David Molina:
Approaching the rank aggregation problem by local search-based metaheuristics. J. Comput. Appl. Math. 354: 445-456 (2019) - [j62]Enrique González Rodrigo, Juan A. Aledo, José A. Gámez:
spark-crowd: A Spark Package for Learning from Crowdsourced Big Data. J. Mach. Learn. Res. 20: 19:1-19:5 (2019) - [j61]Enrique González Rodrigo, Juan A. Aledo, José A. Gámez:
Scaling up the learning-from-crowds GLAD algorithm using instance-difficulty clustering. Prog. Artif. Intell. 8(3): 389-399 (2019) - [j60]Javier Cózar, Alberto Fernández, Francisco Herrera, José A. Gámez:
A Metahierarchical Rule Decision System to Design Robust Fuzzy Classifiers Based on Data Complexity. IEEE Trans. Fuzzy Syst. 27(4): 701-715 (2019) - [j59]Enrique González Rodrigo, Juan A. Aledo, José A. Gámez:
Machine learning from crowds: A systematic review of its applications. WIREs Data Mining Knowl. Discov. 9(2) (2019) - [c68]Juan Carlos Alfaro Jiménez, Enrique González Rodrigo, Juan A. Aledo, José Antonio Gámez:
A Probabilistic Graphical Model-Based Approach for the Label Ranking Problem. ECSQARU 2019: 351-362 - [c67]José Miguel Puerta, Juan A. Aledo, José Antonio Gámez, Jorge D. Laborda:
Structural Fusion/Aggregation of Bayesian Networks via Greedy Equivalence Search Learning Algorithm. ECSQARU 2019: 432-443 - [c66]Luis de la Ossa, Pablo Bermejo, Juan A. Aledo, José A. Gámez, José Miguel Puerta, Cristina Romero-González, Jacinto Arias, Javier Cózar, Enrique González Rodrigo, Juan Ignacio Alonso-Barba, Jesus Martínez-Gómez:
CiDAEN: An Online Data Science Course. HELMeTO 2019: 113-124 - 2018
- [j58]Juan Ignacio Alonso-Barba, Luis de la Ossa, José A. Gámez, José Miguel Puerta:
On the use of local search heuristics to improve GES-based Bayesian network learning. Appl. Soft Comput. 64: 366-376 (2018) - [j57]Juan A. Aledo, José A. Gámez, Alejandro Rosete:
Approaching rank aggregation problems by using evolution strategies: The case of the optimal bucket order problem. Eur. J. Oper. Res. 270(3): 982-998 (2018) - [j56]Pablo Bermejo, José A. Gámez, José Miguel Puerta:
Adapting the CMIM algorithm for multilabel feature selection. A comparison with existing methods. Expert Syst. J. Knowl. Eng. 35(1) (2018) - [j55]Javier Cózar, Luis delaOssa, José A. Gámez:
Learning compact zero-order TSK fuzzy rule-based systems for high-dimensional problems using an Apriori + local search approach. Inf. Sci. 433-434: 1-16 (2018) - [j54]Juan A. Aledo, José A. Gámez, David Molina, Alejandro Rosete:
Consensus-based journal rankings: A complementary tool for bibliometric evaluation. J. Assoc. Inf. Sci. Technol. 69(7): 936-948 (2018) - [j53]Javier Cózar, Francesco Marcelloni, José A. Gámez, Luis de la Ossa:
Building efficient fuzzy regression trees for large scale and high dimensional problems. J. Big Data 5: 49 (2018) - [c65]Enrique González Rodrigo, Juan A. Aledo, José A. Gámez:
CGLAD: Using GLAD in Crowdsourced Large Datasets. IDEAL (1) 2018: 783-791 - [c64]Jacinto Arias, José A. Gámez, José Miguel Puerta:
Bayesian Network Classifiers Under the Ensemble Perspective. PGM 2018: 1-12 - 2017
- [j52]Juan A. Aledo, José A. Gámez, Alejandro Rosete:
Partial evaluation in Rank Aggregation Problems. Comput. Oper. Res. 78: 299-304 (2017) - [j51]Juan A. Aledo, José A. Gámez, Alejandro Rosete:
Utopia in the solution of the Bucket Order Problem. Decis. Support Syst. 97: 69-80 (2017) - [j50]Javier Cózar, José Miguel Puerta, José A. Gámez:
An Application of Dynamic Bayesian Networks to Condition Monitoring and Fault Prediction in a Sensored System: a Case Study. Int. J. Comput. Intell. Syst. 10(1): 176-195 (2017) - [j49]José A. Gámez, Francisco Herrera, José Miguel Puerta:
Guest Editorial: Recent Trends in Intelligent Systems. Int. J. Intell. Syst. 32(2): 107-108 (2017) - [j48]Juan A. Aledo, José A. Gámez, David Molina:
Tackling the supervised label ranking problem by bagging weak learners. Inf. Fusion 35: 38-50 (2017) - [j47]Amparo Alonso-Betanzos, José A. Gámez, Francisco Herrera, José Miguel Puerta, José C. Riquelme:
Volume, variety and velocity in Data Science. Knowl. Based Syst. 117: 1-2 (2017) - [j46]Jacinto Arias, José A. Gámez, José Miguel Puerta:
Learning distributed discrete Bayesian Network Classifiers under MapReduce with Apache Spark. Knowl. Based Syst. 117: 16-26 (2017) - [j45]Antonio Jesús Díaz-Honrubia, Johan De Praeter, Glenn Van Wallendael, José Luis Martínez, Pedro Cuenca, José Miguel Puerta, José A. Gámez:
CTU splitting algorithm for H.264/AVC and HEVC simultaneous encoding. J. Supercomput. 73(1): 190-202 (2017) - [c63]Javier Cózar, Luis de la Ossa, José A. Gámez:
Generation of first-order TSK rules based on the apriori + search approach. CEC 2017: 1675-1682 - [c62]Rafael Rivera-López, Juana Canul-Reich, José A. Gámez, José Miguel Puerta:
OC1-DE: A Differential Evolution Based Approach for Inducing Oblique Decision Trees. ICAISC (1) 2017: 427-438 - 2016
- [j44]Juan A. Aledo, José A. Gámez, David Molina:
Using extension sets to aggregate partial rankings in a flexible setting. Appl. Math. Comput. 290: 208-223 (2016) - [j43]Juan A. Aledo, José A. Gámez, David Molina:
Using metaheuristic algorithms for parameter estimation in generalized Mallows models. Appl. Soft Comput. 38: 308-320 (2016) - [j42]Jacinto Arias, Jesus Martínez-Gómez, José A. Gámez, Alba Garcia Seco de Herrera, Henning Müller:
Medical image modality classification using discrete Bayesian networks. Comput. Vis. Image Underst. 151: 61-71 (2016) - [j41]Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta:
A scalable pairwise class interaction framework for multidimensional classification. Int. J. Approx. Reason. 68: 194-210 (2016) - [j40]Antonio Jesús Díaz-Honrubia, Gabriel Cebrián-Márquez, José Luis Martínez, Pedro Cuenca, José Miguel Puerta, José Antonio Gámez:
Low-complexity heterogeneous architecture for H.264/HEVC video transcoding. J. Real Time Image Process. 12(2): 311-327 (2016) - [j39]Gonzalo Vergara, Juan Ignacio Alonso-Barba, Emilio Soria-Olivas, José A. Gámez, Manuel Domínguez:
Random extreme learning machines to predict electric load in buildings. Prog. Artif. Intell. 5(2): 129-135 (2016) - [j38]Antonio Jesús Díaz-Honrubia, José Luis Martínez, Pedro Cuenca, José Antonio Gámez, José Miguel Puerta:
Adaptive Fast Quadtree Level Decision Algorithm for H.264 to HEVC Video Transcoding. IEEE Trans. Circuits Syst. Video Technol. 26(1): 154-168 (2016) - [c61]Pablo Bermejo, José A. Gámez, José Miguel Puerta, Marco A. Esquivias, Pedro J. Tárraga:
Construction of a Semi-Naive Model to Predict Early Readmission of COPD Patients by Using Quality Care Information. ICDM Workshops 2016: 233-240 - [c60]Juan A. Aledo, José A. Gámez, David Molina, Alejandro Rosete:
FSS-OBOP: Feature subset selection guided by a bucket order consensus ranking. SSCI 2016: 1-8 - [e4]Oscar Luaces, José A. Gámez, Edurne Barrenechea, Alicia Troncoso, Mikel Galar, Héctor Quintián, Emilio Corchado:
Advances in Artificial Intelligence - 17th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2016, Salamanca, Spain, September 14-16, 2016. Proceedings. Lecture Notes in Computer Science 9868, Springer 2016, ISBN 978-3-319-44635-6 [contents] - 2015
- [j37]Jacinto Arias, José A. Gámez, José Miguel Puerta:
Structural Learning of Bayesian Networks Via Constrained Hill Climbing Algorithms: Adjusting Trade-off between Efficiency and Accuracy. Int. J. Intell. Syst. 30(3): 292-325 (2015) - [c59]Antonio Jesús Díaz-Honrubia, José Luis Martínez, Pedro Cuenca, José Antonio Gámez, José Miguel Puerta:
A Data-Driven Probabilistic CTU Splitting Algorithm for Fast H.264/HEVC Video Transcoding. DCC 2015: 449 - [c58]Javier Cózar, Gonzalo Vergara, José A. Gámez, Emilio Soria-Olivas:
Comparing TSK-1 FRBS against SVR for electrical power prediction in buildings. IFSA-EUSFLAT 2015 - [c57]Juan Ignacio Alonso-Barba, Luis de la Ossa, Olivier Regnier-Coudert, John A. W. McCall, José A. Gámez, José Miguel Puerta:
Ant Colony and Surrogate Tree-Structured Models for Orderings-Based Bayesian Network Learning. GECCO 2015: 543-550 - [c56]M. Julia Flores, José A. Gámez:
Impact on Bayesian Networks Classifiers When Learning from Imbalanced Datasets. ICAART (2) 2015: 382-389 - [c55]Gonzalo Vergara, Javier Cózar, Cristina Romero-González, José A. Gámez, Emilio Soria-Olivas:
Comparing ELM Against MLP for Electrical Power Prediction in Buildings. IWINAC (2) 2015: 409-418 - [c54]Jacinto Arias, José A. Gámez, José Miguel Puerta:
Scalable Learning of k-dependence Bayesian Classifiers under MapReduce. TrustCom/BigDataSE/ISPA (2) 2015: 25-32 - [e3]José Miguel Puerta, José A. Gámez, Bernabé Dorronsoro, Edurne Barrenechea, Alicia Troncoso, Bruno Baruque, Mikel Galar:
Advances in Artificial Intelligence - 16th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2015, Albacete, Spain, November 9-12, 2015, Proceedings. Lecture Notes in Computer Science 9422, Springer 2015, ISBN 978-3-319-24597-3 [contents] - 2014
- [j36]Javier Cózar, Luis de la Ossa, José A. Gámez:
Learning TSK-0 linguistic fuzzy rules by means of local search algorithms. Appl. Soft Comput. 21: 57-71 (2014) - [j35]Jens Dalgaard Nielsen, Antonio Salmerón, José A. Gámez:
A tool based on Bayesian networks for supporting geneticists in plant improvement by controlled pollination. Int. J. Approx. Reason. 55(1): 74-83 (2014) - [j34]M. Julia Flores, José A. Gámez, Ana M. Martínez:
Domains of competence of the semi-naive Bayesian network classifiers. Inf. Sci. 260: 120-148 (2014) - [j33]Pablo Bermejo, José A. Gámez, José Miguel Puerta:
Speeding up incremental wrapper feature subset selection with Naive Bayes classifier. Knowl. Based Syst. 55: 140-147 (2014) - [j32]Antonio Fernández, José A. Gámez, Rafael Rumí, Antonio Salmerón:
Data clustering using hidden variables in hybrid Bayesian networks. Prog. Artif. Intell. 2(2-3): 141-152 (2014) - [c53]Antonio Jesús Díaz-Honrubia, José Luis Martínez, José Miguel Puerta, José A. Gámez, Jan De Cock, Pedro Cuenca:
Fast quadtree level decision algorithm for H.264/HEVC transcoder. ICIP 2014: 2497-2501 - [c52]Jacinto Arias, José A. Gámez, Thomas D. Nielsen, José Miguel Puerta:
A Pairwise Class Interaction Framework for Multilabel Classification. Probabilistic Graphical Models 2014: 17-32 - [c51]Javier Cózar, Luis de la Ossa, José A. Gámez:
TSK-0 Fuzzy Rule-Based Systems for High-Dimensional Problems Using the Apriori Principle for Rule Generation. RSCTC 2014: 270-279 - 2013
- [j31]Juan A. Aledo, José A. Gámez, David Molina:
Tackling the rank aggregation problem with evolutionary algorithms. Appl. Math. Comput. 222: 632-644 (2013) - [j30]Juan Ignacio Alonso-Barba, Luis delaOssa, José A. Gámez, José Miguel Puerta:
Scaling up the Greedy Equivalence Search algorithm by constraining the search space of equivalence classes. Int. J. Approx. Reason. 54(4): 429-451 (2013) - [j29]Juan A. Villar, Francisco J. Andujar, José L. Sánchez, Francisco J. Alfaro, José A. Gámez, José Duato:
Obtaining the optimal configuration of high-radix Combined switches. J. Parallel Distributed Comput. 73(9): 1239-1250 (2013) - [c50]Pablo Bermejo, Marta Lucas, José A. Rodríguez-Montes, Pedro J. Tárraga, Javier Lucas, José A. Gámez, José Miguel Puerta:
Single- and Multi-label Prediction of Burden on Families of Schizophrenia Patients. AIME 2013: 115-124 - [c49]Jacinto Arias, José A. Gámez, José Miguel Puerta:
Learning more Accurate Bayesian Networks in the CHC Approach by Adjusting the Trade-Off between Efficiency and Accuracy. CAEPIA 2013: 310-320 - [c48]Juan A. Aledo, José A. Gámez, David Molina:
Computing the Consensus Permutation in Mallows Distribution by Using Genetic Algorithms. IEA/AIE 2013: 102-111 - 2012
- [j28]Jens Dalgaard Nielsen, José A. Gámez, Antonio Salmerón:
Modelling and inference with Conditional Gaussian Probabilistic Decision Graphs. Int. J. Approx. Reason. 53(7): 929-945 (2012) - [j27]Pablo Bermejo, Luis de la Ossa, José A. Gámez, José Miguel Puerta:
Fast wrapper feature subset selection in high-dimensional datasets by means of filter re-ranking. Knowl. Based Syst. 25(1): 35-44 (2012) - [j26]José A. Gámez, Juan L. Mateo, José Miguel Puerta:
One iteration CHC algorithm for learning Bayesian networks: an effective and efficient algorithm for high dimensional problems. Prog. Artif. Intell. 1(4): 329-346 (2012) - [c47]Ana M. Martínez, Geoffrey I. Webb, M. Julia Flores, José A. Gámez:
Non-Disjoint Discretization for Aggregating One-Dependence Estimator Classifiers. HAIS (2) 2012: 151-162 - [c46]Pablo Bermejo, Luis Redondo, Luis delaOssa, Daniel Rodríguez, M. Julia Flores, Carmen Urea, José A. Gámez, Jesus Martínez-Gómez, José Miguel Puerta:
Evaluation of a Thermal-Comfort Control System Using Real Data. KES 2012: 746-755 - [i1]M. Julia Flores, José A. Gámez, Kristian G. Olesen:
Incremental Compilation of Bayesian networks. CoRR abs/1212.2456 (2012) - 2011
- [j25]M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta:
Handling numeric attributes when comparing Bayesian network classifiers: does the discretization method matter? Appl. Intell. 34(3): 372-385 (2011) - [j24]Oscar Cordón, Antonio Fernández-Caballero, José A. Gámez, Frank Hoffmann:
The impact of soft computing for the progress of artificial intelligence. Appl. Soft Comput. 11(2): 1491-1492 (2011) - [j23]José A. Gámez, Juan L. Mateo, José Miguel Puerta:
Learning Bayesian networks by hill climbing: efficient methods based on progressive restriction of the neighborhood. Data Min. Knowl. Discov. 22(1-2): 106-148 (2011) - [j22]Pablo Bermejo, José A. Gámez, José Miguel Puerta:
Improving the performance of Naive Bayes multinomial in e-mail foldering by introducing distribution-based balance of datasets. Expert Syst. Appl. 38(3): 2072-2080 (2011) - [j21]Pablo Bermejo, José A. Gámez, José Miguel Puerta:
Improving Incremental Wrapper-Based Subset Selection via Replacement and Early Stopping. Int. J. Pattern Recognit. Artif. Intell. 25(5): 605-625 (2011) - [j20]M. Julia Flores, José A. Gámez, Kristian G. Olesen:
Incremental Compilation of Bayesian Networks Based on Maximal Prime Subgraphs. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 19(2): 155-191 (2011) - [j19]Pablo Bermejo, José A. Gámez, José Miguel Puerta:
A GRASP algorithm for fast hybrid (filter-wrapper) feature subset selection in high-dimensional datasets. Pattern Recognit. Lett. 32(5): 701-711 (2011) - [j18]María José del Jesus, José A. Gámez, Pedro González, José Miguel Puerta:
On the discovery of association rules by means of evolutionary algorithms. WIREs Data Mining Knowl. Discov. 1(5): 397-415 (2011) - [c45]Juan Ignacio Alonso-Barba, Luis de la Ossa, José A. Gámez, José Miguel Puerta:
Scaling Up the Greedy Equivalence Search Algorithm by Constraining the Search Space of Equivalence Classes. ECSQARU 2011: 194-205 - [c44]Arcadio Rubio, José Antonio Gámez:
Flexible learning of k-dependence Bayesian network classifiers. GECCO 2011: 1219-1226 - [c43]Pablo Bermejo, Luis de la Ossa, José A. Gámez, José Miguel Puerta:
Enhancing Incremental Feature Subset Selection in High-Dimensional Databases by Adding a Backward Step. ISCIS 2011: 93-97 - [c42]Javier Cózar, Luis delaOssa, José A. Gámez:
Learning heterogeneus cooperative linguistic fuzzy rules using local search: Enhancing the COR search space. ISDA 2011: 475-480 - [c41]M. Julia Flores, José A. Gámez, Ana M. Martínez, Antonio Salmerón:
Mixture of truncated exponentials in supervised classification: Case study for the naive bayes and averaged one-dependence estimators classifiers. ISDA 2011: 593-598 - [c40]Pablo Bermejo, Luis de la Ossa, José A. Gámez, José Miguel Puerta:
A study on different backward feature selection criteria over high-dimensional databases. ISDA 2011: 1300-1305 - [e2]José Antonio Lozano, José A. Gámez, José A. Moreno:
Advances in Artificial Intelligence - 14th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2011, La Laguna, Spain, November 7-11, 2011. Proceedings. Lecture Notes in Computer Science 7023, Springer 2011, ISBN 978-3-642-25273-0 [contents] - 2010
- [c39]Jesus Martínez-Gómez, José A. Gámez, Ismael García-Varea:
Comparing Cellular and Panmictic Genetic Algorithms for Real-Time Object Detection. EvoApplications (1) 2010: 261-271 - [c38]Luis delaOssa, José A. Gámez, José Miguel Puerta:
Learning cooperative linguistic fuzzy rules using fast local search algorithms. FUZZ-IEEE 2010: 1-8 - [c37]Jesus Martínez-Gómez, Alejandro Jiménez-Picazo, José A. Gámez, Ismael García-Varea:
Combining Image Invariant Features and Clustering Techniques for Visual Place Classification. ICPR Contests 2010: 200-209 - [c36]M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta:
Analyzing the Impact of the Discretization Method When Comparing Bayesian Classifiers. IEA/AIE (1) 2010: 570-579 - [c35]Pablo Bermejo, José A. Gámez, José Miguel Puerta:
Improving Incremental Wrapper-Based Feature Subset Selection by Using Re-ranking. IEA/AIE (1) 2010: 580-589
2000 – 2009
- 2009
- [j17]Luis de la Ossa, José A. Gámez, José Miguel Puerta:
Learning weighted linguistic fuzzy rules by using specifically-tailored hybrid estimation of distribution algorithms. Int. J. Approx. Reason. 50(3): 541-560 (2009) - [j16]María José del Jesus, José A. Gámez, José Miguel Puerta:
Evolutionary and metaheuristics based data mining. Soft Comput. 13(3): 209-212 (2009) - [c34]Luis de la Ossa, José A. Gámez, Juan L. Mateo, José Miguel Puerta:
Avoiding premature convergence in estimation of distribution algorithms. IEEE Congress on Evolutionary Computation 2009: 455-462 - [c33]Pablo Bermejo, José A. Gámez, José Miguel Puerta:
Incremental Wrapper-based subset Selection with replacement: An advantageous alternative to sequential forward selection. CIDM 2009: 367-374 - [c32]M. Julia Flores, José A. Gámez, Jens Dalgaard Nielsen:
The PDG-Mixture Model for Clustering. DaWaK 2009: 378-389 - [c31]M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta:
HODE: Hidden One-Dependence Estimator. ECSQARU 2009: 481-492 - [c30]M. Julia Flores, José A. Gámez, Ana M. Martínez, José Miguel Puerta:
GAODE and HAODE: two proposals based on AODE to deal with continuous variables. ICML 2009: 313-320 - [c29]Pablo Bermejo, Frank Hopfgartner, José A. Gámez, José Miguel Puerta, Joemon M. Jose:
Comparison of balancing techniques for multimedia IR over imbalanced datasets. ISCIS 2009: 674-679 - [c28]Jesus Martínez-Gómez, José A. Gámez, Ismael García-Varea, Vicente Matellán:
Using Genetic Algorithms for Real-Time Object Detection. RoboCup 2009: 215-227 - 2008
- [j15]Luis Rodríguez, Ismael García-Varea, José A. Gámez:
On the application of different evolutionary algorithms to the alignment problem in statistical machine translation. Neurocomputing 71(4-6): 755-765 (2008) - [j14]