Papers published in Journals (R. Alcalá)

Number of Results: 44

Jump to Year: 2024, 2023, 2022, 2020, 2019, 2018, 2017, 2014, 2013, 2012, 2011, 2010, 2009, 2007, 2006, 2005, 2003, 2001

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2024 (1)

• [3100] A. Torres-Martos, A. Anguita-Ruiz, M. Bustos-Aibar, A. Ramírez-Mena, M. Arteaga, G. Bueno, R. Leis, C.M. Aguilera, R. Alcalá, J. Alcalá-Fdez. Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study. Artificial Intelligence in Medicine 156 (2024) 102962. doi: 10.1016/j.artmed.2024.102962
  

2023 (3)

• [3030] Á. Torres-Martos, M. Bustos-Aibar, A. Ramírez-Mena, S. Cámara-Sánchez, A. Anguita-Ruiz, R. Alcalá, C.M. Aguilera, J. Alcalá-Fdez. Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity. Genes 14:2 (2023) 248. doi: 10.3390/genes14020248
  
• [3043] C. Biedma-Rdguez, M.J. Gacto, A. Anguita-Ruiz, R. Alcalá, C.M. Aguilera, J. Alcalá-Fdez. Learning positive-negative rule-based fuzzy associative classifiers with a good trade-off between complexity and accuracy. Fuzzy Sets and Systems 465 (2023) 108511. doi: 10.1016/j.fss.2023.03.014
  
• [3054] M. Arteaga, M.J. Gacto, M. Galende, J. Alcalá-Fdez, R. Alcalá. Enhancing soft computing techniques to actively address imbalanced regression problems. Expert Systems with Applications 234 (2023) 121011. doi: 10.1016/j.eswa.2023.121011
  

2022 (2)

• [2961] A. Segura-Delgado, A. Anguita-Ruiz, R. Alcalá, J. Alcalá-Fdez. Mining high average-utility sequential rules to identify high-utility gene expression sequences in longitudinal human studies. Expert Systems with Applications 193 (2022) 116411. doi: 10.1016/j.eswa.2021.116411
  
• [2998] C. Biedma-Rdguez, M.J. Gacto, A. Anguita-Ruiz, J. Alcalá-Fdez, R. Alcalá. Transparent but Accurate Evolutionary Regression Combining New Linguistic Fuzzy Grammar and a Novel Interpretable Linear Extension. International Journal of Fuzzy Systems 24 (2022) 3082–3103. doi: 10.1007/s40815-022-01324-w
  

2020 (2)

• [2799] A. Anguita-Ruiz, A. Segura-Delgado, R. Alcalá, C.M. Aguilera, J. Alcalá-Fdez. eXplainable Artificial Intelligence (XAI) for the identification of biologically relevant gene expression patterns in longitudinal human studies, insights from obesity research. PLoS Computational Biology 16:4 (2020) 1-34. doi: 10.1371/journal.pcbi.1007792
  
• [2800] A. Segura-Delgado, M.J. Gacto, R. Alcalá, J. Alcalá-Fdez. Temporal association rule mining: An overview considering the time variable as an integral or implied component. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery 10:4 (2020) e1367. doi: 10.1002/widm.1367
  

2019 (1)

• [2695] M.J. Gacto, J.M. Soto-Hidalgo, J. Alcalá-Fdez, R. Alcalá. Experimental Study on 164 Algorithms Available in Software Tools for Solving Standard Non-Linear Regression Problems. IEEE Access 7 (2019) 108916-108939. doi: 10.1109/ACCESS.2019.2933261
  

2018 (1)

• [2422] R. Alcalá, M.J. Gacto, J. Alcalá-Fdez. Evolutionary data mining and applications: A revision on the most cited papers from the last 10 years (2007-2017). Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery 8:2 (2018) 1-17. doi: 10.1002/widm.1239
  

2017 (1)

• [2307] J. Alcalá-Fdez, R. Alcalá, S. González, Y. Nojima, S. García. Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification. IEEE Transactions on Fuzzy Systems 25:6 (2017) 1376-1390. doi: 10.1109/TFUZZ.2017.2718491
  

2014 (3)

• [1732] M.J. Gacto, M. Galende, R. Alcalá, F. Herrera. METSK-HDe: A Multiobjective Evolutionary Algorithm to learn accurate TSK-fuzzy Systems in High-Dimensional and Large-Scale Regression Problems. Information Sciences 276 (2014) 63-79. doi: 10.1016/j.ins.2014.02.047
  
• [1771] M. Galende, M.J. Gacto, G. Sainz, R. Alcalá. Comparison and Design of Interpretable Linguistic vs. Scatter FRBSs: GM3M Generalization and New Rule Meaning Index (RMI) for Global Assessment and Local Pseudo-Linguistic Representation. Information Sciences 282 (2014) 190-213. doi: 10.1016/j.ins.2014.05.023
  
• [1816] M. Fazzolari, R. Alcalá, F. Herrera. A Multi-Objective Evolutionary Method for Learning Granularities based on Fuzzy Discretization to Improve the Accuracy-Complexity trade-off of Fuzzy Rule-Based Classification Systems: D-MOFARC Algorithm. Applied Soft Computing 24 (2014) 470-481.
  

2013 (2)

• [1521] M. Fazzolari, R. Alcalá, Y. Nojima, H. Ishibuchi, F. Herrera. A Review of the Application of Multi-Objective Evolutionary Fuzzy Systems: Current Status and Further Directions. IEEE Transactions on Fuzzy Systems, 21:1 (2013) 45-65.
  COMPLEMENTARY MATERIAL to the paper: links to the papers with doi, new contributions, etc
  
  
• [1680] M. Fazzolari, B. Giglio, R. Alcalá, F. Marcelloni, F. Herrera. A study on the application of instance selection techniques in genetic fuzzy rule-based classification systems: Accuracy-complexity trade-off. Knowledge-Based Systems 54 (2013) 32-41. doi: 10.1016/j.knosys.2013.07.011
  

2012 (3)

• [1328] M.J. Gacto, R. Alcalá, F. Herrera. A Multi-Objective Evolutionary Algorithm for an Effective Tuning of Fuzzy Logic Controllers in Heating, Ventilating and Air Conditioning Systems. Applied Intelligence 36:2 (2012) 330-347. doi: 10.1007/s10489-010-0264-x
  
• [1336] F. Chávez, F. Fernández, R. Alcalá, J. Alcalá-Fdez, G. Olague, F. Herrera. Hybrid Laser Pointer Detection Algorithm Based on Template Matching and Fuzzy Rule-Based Systems for Domotic Control in Real Home Enviroments. Applied Intelligence 36:2 (2012) 407-423. doi: 10.1007/s10489-010-0268-6
  
• [1419] F. Chávez, F. Fernández, M.J. Gacto, R. Alcalá. Automatic Laser Pointer Detection Algorithm for Environment Control Device Systems Based on Template Matching and Genetic Tuning of Fuzzy Rule-Based Systems. International Journal of Computational Intelligence Systems 5:2 (2012) 368-386. doi: 10.1080/18756891.2012.685327
  

2011 (4)

• [1329] R. Alcalá, Y. Nojima, F. Herrera, H. Ishibuchi. Multiobjective Genetic Fuzzy Rule Selection of Single Granularity-Based Fuzzy Classification Rules and its Interaction with the Lateral Tuning of Membership Functions. Soft Computing 15:12 (2011) 2303-2318. doi: 10.1007/s00500-010-0671-2
  
• [1380] R. Alcalá, M.J. Gacto, F. Herrera. A Fast and Scalable Multi-Objective Genetic Fuzzy System for Linguistic Fuzzy Modeling in High-Dimensional Regression Problems. IEEE Transactions on Fuzzy Systems 19:4 (2011) 666-681. doi: 10.1109/TFUZZ.2011.2131657
  COMPLEMENTARY MATERIAL to the paper: dataset partitions, results, figures, etc
  
  
• [1383] M.J. Gacto, R. Alcalá, F. Herrera. Interpretability of Linguistic Fuzzy Rule-Based Systems: An Overview of Interpretability Measures. Information Sciences, 181:20 (2011) 4340–4360.
  COMPLEMENTARY MATERIAL to the paper: links to the papers with doi, new contributions, etc
  
  
• [1402] J. Alcalá-Fdez, R. Alcalá, F. Herrera. A Fuzzy Association Rule-Based Classification Model for High-Dimensional Problems with Genetic Rule Selection and Lateral Tuning. IEEE Transactions on Fuzzy Systems 19:5 (2011) 857-872. doi: 10.1109/TFUZZ.2011.2147794
  

2010 (2)

• [1071] M. Mucientes, J. Alcalá-Fdez, R. Alcalá, J. Casillas. A case study for learning behaviors in mobile robotics by evolutionary fuzzy systems. Expert Systems With Applications 37:2 (2010) 1471–1493. doi: 10.1016/j.eswa.2009.06.095
  
• [1228] M.J. Gacto, R. Alcalá, F. Herrera. Integration of an Index to Preserve the Semantic Interpretability in the Multi-Objective Evolutionary Rule Selection and Tuning of Linguistic Fuzzy Systems. IEEE Transactions on Fuzzy Systems 18:3 (2010) 515-531. doi: 10.1109/TFUZZ.2010.2041008
  

2009 (6)

• [0650] M. Mucientes, R. Alcalá, J. Alcalá-Fdez, J. Casillas. Learning Weighted Linguistic Rules to Control an Autonomous Robot. International Journal of Intelligent Systems 24:3 (2009) 226–251. doi: 10.1002/int.20334
  
• [0671] R. Alcalá, J. Alcalá-Fdez, M.J. Gacto, F. Herrera. Improving Fuzzy Logic Controllers Obtained by Experts: A Case Study in HVAC Systems. Applied Intelligence 31:1 (2009) 15-30. doi: 10.1007/s10489-007-0107-6
  
• [0838] J. Alcalá-Fdez, R. Alcalá, M.J. Gacto, F. Herrera. Learning the Membership Function Contexts for Mining Fuzzy Association Rules by Using Genetic Algorithms. Fuzzy Sets and Systems 160:7 (2009) 905-921. doi: 10.1016/j.fss.2008.05.012
  
• [0837] M.J. Gacto, R. Alcalá, F. Herrera. Adaptation and Application of Multi-Objective Evolutionary Algorithms for Rule Reduction and Parameter Tuning of Fuzzy Rule-Based Systems. Soft Computing 13:5 (2009) 419-436. doi: 10.1007/s00500-008-0359-z
  
• [1054] R. Alcalá, P. Ducange, F. Herrera, B. Lazzerini, F. Marcelloni. A Multi-Objective Evolutionary Approach to Concurrently Learn Rule and Data Bases of Linguistic Fuzzy Rule-Based Systems. IEEE Transactions on Fuzzy Systems 17:5 (2009) 1106-1122. doi: 10.1109/TFUZZ.2009.2023113
  
• [1079] I. Robles, R. Alcalá, J.M. Benítez, F. Herrera. Evolutionary Parallel and Gradually Distributed Lateral Tuning of Fuzzy Rule-Based Systems. Evolutionary Intelligence 2 (2009) 5-19. doi: 10.1007/s12065-009-0025-0
  

2007 (5)

• [0344] R. Alcalá, J. Alcalá-Fdez, J. Casillas, O. Cordón, F. Herrera. Local Identification of Prototypes for Genetic Learning of Accurate TSK Fuzzy Rule-Based Systems. International Journal of Intelligent Systems 22:9 (2007) 909-941. doi: 10.1002/int.20232
  
• [0651] R. Alcalá, M.J. Gacto, F. Herrera, J. Alcalá-Fdez. A Multi-objective Genetic Algorithm for Tuning and Rule Selection to Obtain Accurate and Compact Linguistic Fuzzy Rule-Based Systems. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15:5 (2007) 539–557. doi: 10.1142/S0218488507004868
  
• [0556] R. Alcalá, J. Alcalá-Fdez, M.J. Gacto, F. Herrera. Rule Base Reduction and Genetic Tuning of Fuzzy Systems based on the Linguistic 3-Tuples Representation. Soft Computing 11:5 (2007) 401-419. doi: 10.1007/s00500-006-0106-2
  
• [0572] R. Alcalá, J. Alcalá-Fdez, F. Herrera, J. Otero. Genetic Learning of Accurate and CompactFuzzy Rule Based Systems Based on the 2-Tuples LinguisticRepresentation. International Journal of Approximate Reasoning 44:1 (2007) 45-64. doi: 10.1016/j.ijar.2006.02.007
  
• [0598] R. Alcalá, J. Alcalá-Fdez, F. Herrera. A Proposal for the Genetic Lateral Tuning of Linguistic Fuzzy Systems and its Interaction with Rule Selection. IEEE Transactions on Fuzzy Systems 15:4 (2007) 616-635. doi: 10.1109/TFUZZ.2006.889880
  

2006 (1)

• [0343] R. Alcalá, J. Alcalá-Fdez, J. Casillas, O. Cordón, F. Herrera. Hybrid learning models to get the interpretability-accuracy trade-off in Fuzzy Modelling. Soft Computing 10:9 (2006) 717-734. doi: 10.1007/s00500-005-0002-1
  

2005 (1)

• [0340] R. Alcalá, J. Casillas, O. Cordón, A. González, F. Herrera. A Genetic Rule Weighting and Selection Process for Fuzzy Control of Heating, Ventilating and Air Conditioning Systems. Engineering Applications of Artificial Intelligence 18:3 (2005) 279-296. doi: 10.1016/j.engappai.2004.09.007
  

2003 (3)

• [0001] R. Alcalá, J.M. Benítez, J. Casillas, O. Cordón, R. Pérez. Fuzzy Control of HVAC Systems Optimized by Genetic Algorithms. Applied Intelligence 18:2 (2003) 155-177. doi: 10.1023/A:1021986309149
  
• [0011] R. Alcalá, J.R. Cano, O. Cordón, F. Herrera, P. Villar, I. Zwir. Linguistic Modeling with Hierarchical Systems of Weighted Linguistic Rules. International Journal of Approximate Reasoning 32:2-3 (2003) 187-215. doi: 10.1016/S0888-613X(02)00083-X
  
• [0013] R. Alcalá, J. Casillas, O. Cordón, F. Herrera. Linguistic Modeling with Weighted Double-Consequent Fuzzy Rules Based on Cooperative Coevolutionary Learning. Integrated Computer Aided Engineering 10 (4) (2003) 343-355.
  

2001 (3)

• [0017] R. Alcalá, J. Casillas, O. Cordón, F. Herrera. Building Fuzzy Graphs: Features and Taxonomy of Learning Non-Grid-Oriented Fuzzy Rule-Based Systems. International Journal of Intelligent Fuzzy Systems, 11 (2001) 99-119.
  
• [0019] R. Alcalá, J. Casillas, J.L. Castro, A. Gonzalez, F. Herrera. A Multicriteria Genetic Tuning for Fuzzy Logic Controllers. Mathware and Soft Computing, Vol. 8:2 (2001) 179-201.
  
• [0035] R. Alcalá, J. Casillas, O. Cordón, F. Herrera. Improvement to the Cooperative Rules Methodology by Using the Ant Colony System Algorithm. Mathware and Soft Computing Vol 8:3 (2001) 321-335.