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Raúl Pérez
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2020 – today
- 2024
- [j29]Leonardo Jara
, Antonio González
QChi: A Faster Classification Algorithm Based on Wang-Mendel Algorithm. IEEE Trans. Fuzzy Syst. 32(4): 2261-2271 (2024) - [i3]Carlos Núñez-Molina, Juan Fernández-Olivares, Raúl Pérez:
Learning to Select Goals in Automated Planning with Deep-Q Learning. CoRR abs/2406.14779 (2024) - 2023
- [i2]Ignacio Vellido, Juan Fdez-Olivares, Raúl Pérez:
Discovering and Explaining Driver Behaviour under HoS Regulations. CoRR abs/2301.05082 (2023) - 2022
- [j28]Leonardo Jara
Efficient inference models for classification problems with a high number of fuzzy rules. Appl. Soft Comput. 115: 108164 (2022) - [j27]Carlos Núñez-Molina
Learning to select goals in Automated Planning with Deep-Q Learning. Expert Syst. Appl. 202: 117265 (2022) - [j26]Juan Carlos Gámez-Granados
Automating the decision making process of Todd's age estimation method from the pubic symphysis with explainable machine learning. Inf. Sci. 612: 514-535 (2022) - [c25]Leonardo Jara, Antonio González, Raúl Pérez:
A new multi-rules approach to improve the performance of the Chi fuzzy rule classification algorithm. FUZZ-IEEE 2022: 1-6 - [c24]Ignacio Vellido-Expósito
Analyzing Driver Behavior Compliance under HoS Regulations. VEHITS 2022: 463-470 - 2021
- [j25]José Á. Segura-Muros
Discovering relational and numerical expressions from plan traces for learning action models. Appl. Intell. 51(11): 7973-7989 (2021) - [c23]Carlos Núñez-Molina
A Proposal to Integrate Deep Q-Learning with Automated Planning to Improve the Performance of a Planning-Based Agent. CAEPIA 2021: 23-32 - [i1]José Á. Segura-Muros, Juan Fernández-Olivares, Raúl Pérez:
Learning Numerical Action Models from Noisy Input Data. CoRR abs/2111.04997 (2021) - 2020
- [c22]Juan Fernández-Olivares, Raúl Pérez:
Driver Activity Recognition by Means of Temporal HTN Planning. ICAPS 2020: 375-383 - [c21]Leonardo Jara
A preliminary study to apply the Quine McCluskey algorithm for fuzzy rule base minimization. FUZZ-IEEE 2020: 1-6
2010 – 2019
- 2019
- [j24]Juan Carlos Gámez
An approximation to solve regression problems with a genetic fuzzy rule ordinal algorithm. Appl. Soft Comput. 78: 13-28 (2019) - [j23]Antonio González
An Incremental Approach to Address Big Data Classification Problems Using Cognitive Models. Cogn. Comput. 11(3): 347-366 (2019) - [c20]Juan Carlos Gámez-Granados, José Manuel Soto-Hidalgo, Giovanni Acampora
A software implementation of the fuzzy rule learning algorithm NSLVOrd for ordinal classification into KEEL. FUZZ-IEEE 2019: 1-6 - [c19]Antonio González
Reasoning Methods in Fuzzy Rule-based Classification Systems for Big Data Problems. IoTBDS 2019: 255-261 - 2018
- [c18]José Á. Segura-Muros
Learning Planning Action Models with Numerical Information and Logic Relationships Using Classification Techniques. CAEPIA 2018: 372-382 - [c17]José Á. Segura-Muros
Using Inductive Rule Learning Techniques to Learn Planning Domains. IPMU (3) 2018: 642-656 - 2017
- [c16]Rocío Romero-Záliz
Incremental fuzzy learning algorithms in big data problems: A study on the size of learning subsets. FUZZ-IEEE 2017: 1-6 - 2016
- [j22]Juan Carlos Gámez
Ordinal classification based on the sequential covering strategy. Int. J. Approx. Reason. 76: 96-110 (2016) - [c15]Juan Carlos Gámez
On the use of an incremental approach to learn fuzzy classification rules for big data problems. FUZZ-IEEE 2016: 1413-1420 - [c14]Nikolaos L. Tsakiridis
Extensions of the DECO3R algorithm for generating compact and cooperating Fuzzy Rule-based Classification Systems. FUZZ-IEEE 2016: 1429-1436 - 2015
- [j21]David García, Juan Carlos Gámez
An interpretability improvement for fuzzy rule bases obtained by the iterative rule learning approach. Int. J. Approx. Reason. 67: 37-58 (2015) - [j20]Enrique Leyva, Antonio González Muñoz
Three new instance selection methods based on local sets: A comparative study with several approaches from a bi-objective perspective. Pattern Recognit. 48(4): 1523-1537 (2015) - [j19]Enrique Leyva, Antonio González Muñoz
A Set of Complexity Measures Designed for Applying Meta-Learning to Instance Selection. IEEE Trans. Knowl. Data Eng. 27(2): 354-367 (2015) - [c13]David García, Dimitris G. Stavrakoudis, Antonio González Muñoz, Raúl Pérez, John B. Theocharis:
A Fuzzy Rule-Based Feature Construction Approach Applied to Remotely Sensed Imagery. IFSA-EUSFLAT 2015 - [c12]David García, Juan Carlos Gámez, Antonio González Muñoz
Using a sequential covering strategy for discovering fuzzy rules incrementally. FUZZ-IEEE 2015: 1-8 - 2014
- [j18]David García, Antonio González Muñoz
Overview of the SLAVE learning algorithm: A review of its evolution and prospects. Int. J. Comput. Intell. Syst. 7(6): 1194-1221 (2014) - [j17]Enrique Leyva, Yoel Caises, Antonio González Muñoz
On the use of meta-learning for instance selection: An architecture and an experimental study. Inf. Sci. 266: 16-30 (2014) - [j16]David García, Antonio González Muñoz
A feature construction approach for genetic iterative rule learning algorithm. J. Comput. Syst. Sci. 80(1): 101-117 (2014) - 2013
- [j15]Enrique Leyva, Antonio González Muñoz
Knowledge-based instance selection: A compromise between efficiency and versatility. Knowl. Based Syst. 47: 65-76 (2013) - [c11]Enrique Leyva, Antonio González Muñoz
A Genetic Tuned Fuzzy Classifier Based on Prototypes. EUSFLAT Conf. 2013 - [c10]David García, Antonio González Muñoz
A new iterative model to simplify the knowledge extracted on a fuzzy rule-based learning algorithm. FUZZ-IEEE 2013: 1-7 - [c9]David García, Antonio González Muñoz
An empirical study about the behavior of a genetic learning algorithm on searching spaces pruned by a completeness condition. GEFS 2013: 8-15 - 2012
- [j14]Antonio González Muñoz
An Efficient Inductive Genetic Learning Algorithm for Fuzzy Relational Rules. Int. J. Comput. Intell. Syst. 5(2): 212-230 (2012) - 2011
- [j13]Yoel Caises, Antonio González Muñoz
Combining instance selection methods based on data characterization: An approach to increase their effectiveness. Inf. Sci. 181(20): 4780-4798 (2011) - [c8]David García, Antonio González Muñoz
A two-step approach of feature construction for a genetic learning algorithm. FUZZ-IEEE 2011: 1255-1262 - [c7]David García, Antonio González Muñoz
An iterative strategy for feature construction on a fuzzy rule-based learning algorithm. ISDA 2011: 1235-1240 - 2010
- [c6]Yoel Caises, Enrique Leyva, Antonio González Muñoz
A genetic learning of fuzzy relational rules. FUZZ-IEEE 2010: 1-8 - [c5]Yoel Caises, Enrique Leyva, Antonio González Muñoz
An extension of the Genetic Iterative Approach for learning rule subsets. GEFS 2010: 63-67
2000 – 2009
- 2009
- [c4]Yoel Caises, Antonio González Muñoz
SCIS: Combining Instance Selection Methods to Increase Their Effectiveness over a Wide Range of Domains. IDEAL 2009: 17-24 - 2008
- [j12]J. Luis Mato, Miguel Pereira, Juan J. Rodríguez-Andina
Distortion Mitigation in RF Power Amplifiers Through FPGA-Based Amplitude and Phase Predistortion. IEEE Trans. Ind. Electron. 55(11): 4085-4093 (2008) - 2007
- [j11]Jorge Casillas
Special Issue on Genetic Fuzzy Systems and the Interpretability-Accuracy Trade-off. Int. J. Approx. Reason. 44(1): 1-3 (2007) - [c3]Enrique Alba, Alejandro Cervantes
Metaheuristic Approaches for Optimal Broadcasting Design in Metropolitan MANETs. EUROCAST 2007: 755-763 - 2004
- [j10]Eugenio Aguirre
A fuzzy perceptual model for map building and navigation of mobile robots. Integr. Comput. Aided Eng. 11(3): 239-258 (2004) - 2003
- [j9]Rafael Alcalá
Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms. Appl. Intell. 18(2): 155-177 (2003) - [c2]Brian Carse, Anthony G. Pipe, Ingo Renners, Adolf Grauel, Antonio Fernandez Gómez-Skarmeta, Fernando Jiménez, Gracia Sánchez, Oscar Cordón, Francisco Herrera, Fernando A. C. Gomide, Igor Walter, Antonio González Muñoz, Raúl Pérez:
Current issues and future directions in evolutionary fuzzy systems research. EUSFLAT Conf. 2003: 81-87 - [c1]Eugenio Aguirre, Antonio González Muñoz, Raúl Pérez:
An inductive approach for learning fuzzy relation rules. EUSFLAT Conf. 2003: 88-93 - 2001
- [j8]Luis A. Castillo, Antonio González Muñoz
Including a simplicity criterion in the selection of the best rule in a genetic fuzzy learning algorithm. Fuzzy Sets Syst. 120(2): 309-321 (2001) - [j7]Antonio González Muñoz
An experimental study about the search mechanism in the SLAVE learning algorithm: Hill-climbing methods versus genetic algorithms. Inf. Sci. 136(1-4): 159-174 (2001) - [j6]Antonio González Muñoz
Selection of relevant features in a fuzzy genetic learning algorithm. IEEE Trans. Syst. Man Cybern. Part B 31(3): 417-425 (2001)
1990 – 1999
- 1999
- [j5]Antonio González Muñoz
A Study About the Inclusion of Linguistic Hedges in a Fuzzy Rule Learning Algorithm. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 7(3): 257-266 (1999) - [j4]Antonio González Muñoz
SLAVE: a genetic learning system based on an iterative approach. IEEE Trans. Fuzzy Syst. 7(2): 176-191 (1999) - 1998
- [j3]Antonio González Muñoz, Raúl Pérez:
Un algoritmo de aprendizaje de reglas basado en el método genético iterativo. Inteligencia Artif. 2(5): 44-49 (1998) - [j2]Antonio González
Completeness and consistency conditions for learning fuzzy rules. Fuzzy Sets Syst. 96(1): 37-51 (1998) - [j1]Antonio González Muñoz
A fuzzy theory refinement algorithm. Int. J. Approx. Reason. 19(3-4): 193-220 (1998)
Coauthor Index
aka: Juan Fdez-Olivares
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