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María José del Jesús: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Francisco José Berlanga, María José del Jesús, María José Gacto, Francisco Herrera
    A Genetic-Programming-Based Approach for the Learning of Compact Fuzzy Rule-Based Classification Systems. [Citation Graph (0, 0)][DBLP]
    ICAISC, 2006, pp:182-191 [Conf]
  2. Francisco José Berlanga, María José del Jesús, Pedro González, Francisco José Herrera, Mikel Mesonero
    Multiobjective Evolutionary Induction of Subgroup Discovery Fuzzy Rules: A Case Study in Marketing. [Citation Graph (0, 0)][DBLP]
    Industrial Conference on Data Mining, 2006, pp:337-349 [Conf]
  3. M. Navío, J. J. Aguilera, María José del Jesús, R. González, Francisco Herrera, C. Iríbar
    Feature Selection Algorithms Applied to Parkinson's Disease. [Citation Graph (0, 0)][DBLP]
    ISMDA, 2001, pp:195-200 [Conf]
  4. Antonio Jesús Rivera Rivas, Julio Ortega, Ignacio Rojas, María José del Jesús
    Co-evolutionary Algorithm for RBF by Self-Organizing Population of Neurons. [Citation Graph (0, 0)][DBLP]
    IWANN (1), 2003, pp:470-477 [Conf]
  5. Oscar Cordón, María José del Jesús, Francisco Herrera, Manuel Lozano
    Modelado cualitativo utilizando una metodología evolutiva de aprendizaje iterativo de bases de reglas difusas. [Citation Graph (0, 0)][DBLP]
    Inteligencia Artificial, Revista Iberoamericana de Inteligencia Artificial, 1998, v:5, n:, pp:56-61 [Journal]
  6. Oscar Cordón, María José del Jesús, Francisco Herrera
    A proposal on reasoning methods in fuzzy rule-based classification systems. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 1999, v:20, n:1, pp:21-45 [Journal]
  7. Luciano Sánchez, Jorge Casillas, Oscar Cordón, María José del Jesús
    Some relationships between fuzzy and random set-based classifiers and models. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 2002, v:29, n:2, pp:175-213 [Journal]
  8. Jorge Casillas, Francisco Herrera, R. Pérez, María José del Jesús, Pedro Villar
    Special Issue on Genetic Fuzzy Systems and the Interpretability-Accuracy Trade-off. [Citation Graph (0, 0)][DBLP]
    Int. J. Approx. Reasoning, 2007, v:44, n:1, pp:1-3 [Journal]
  9. Jorge Casillas, Oscar Cordón, María José del Jesús, Francisco Herrera
    Genetic feature selection in a fuzzy rule-based classification system learning process for high-dimensional problems. [Citation Graph (0, 0)][DBLP]
    Inf. Sci., 2001, v:136, n:1-4, pp:135-157 [Journal]
  10. Antonio Jesús Rivera Rivas, Ignacio Rojas, Julio Ortega, María José del Jesús
    A new hybrid methodology for cooperative-coevolutionary optimization of radial basis function networks. [Citation Graph (0, 0)][DBLP]
    Soft Comput., 2007, v:11, n:7, pp:655-668 [Journal]
  11. María José del Jesús, Frank Hoffmann, Luis Junco Navascués, Luciano Sánchez
    Induction of fuzzy-rule-based classifiers with evolutionary boosting algorithms. [Citation Graph (0, 0)][DBLP]
    IEEE T. Fuzzy Systems, 2004, v:12, n:3, pp:296-308 [Journal]
  12. Jorge Casillas, Oscar Cordón, María José del Jesús, Francisco Herrera
    Genetic tuning of fuzzy rule deep structures preserving interpretability and its interaction with fuzzy rule set reduction. [Citation Graph (0, 0)][DBLP]
    IEEE T. Fuzzy Systems, 2005, v:13, n:1, pp:13-29 [Journal]
  13. M. Dolores Pérez-Godoy, Antonio Jesús Rivera Rivas, María José del Jesús, Ignacio Rojas
    CoEvRBFN: An Approach to Solving the Classification Problem with a Hybrid Cooperative-Coevolutive Algorithm. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:324-332 [Conf]
  14. Alberto Fernández, Salvador García, María José del Jesús, Francisco Herrera
    A Study on the Use of the Fuzzy Reasoning Method Based on the Winning Rule vs. Voting Procedure for Classification with Imbalanced Data Sets. [Citation Graph (0, 0)][DBLP]
    IWANN, 2007, pp:375-382 [Conf]
  15. Alberto Fernández, Salvador García, Francisco Herrera, María José del Jesús
    An Analysis of the Rule Weights and Fuzzy Reasoning Methods for Linguistic Rule Based Classification Systems Applied to Problems with Highly Imbalanced Data Sets. [Citation Graph (0, 0)][DBLP]
    WILF, 2007, pp:170-178 [Conf]

  16. Niching Genetic Feature Selection Algorithms Applied to the Design of Fuzzy Rule-based Classification Systems. [Citation Graph (, )][DBLP]


  17. An Study on Data Mining Methods for Short-Term Forecasting of the Extra Virgin Olive Oil Price in the Spanish Market. [Citation Graph (, )][DBLP]


  18. A Short Study on the Use of Genetic 2-Tuples Tuning for Fuzzy Rule Based Classification Systems in Imbalanced Data-Sets. [Citation Graph (, )][DBLP]


  19. Study of the Robustness of a Meta-Algorithm for the Estimation of Parameters in Artificial Neural Networks Design. [Citation Graph (, )][DBLP]


  20. Multi-class Imbalanced Data-Sets with Linguistic Fuzzy Rule Based Classification Systems Based on Pairwise Learning. [Citation Graph (, )][DBLP]


  21. E-tsRBF: Preliminary Results on the Simultaneous Determination of Time-Lags and Parameters of Radial Basis Function Neural Networks for Time Series Forecasting. [Citation Graph (, )][DBLP]


  22. A Preliminar Analysis of CO2RBFN in Imbalanced Problems. [Citation Graph (, )][DBLP]


  23. Parallelizing the Design of Radial Basis Function Neural Networks by Means of Evolutionary Meta-algorithms. [Citation Graph (, )][DBLP]


  24. Improving the Performance of Fuzzy Rule Based Classification Systems for Highly Imbalanced Data-Sets Using an Evolutionary Adaptive Inference System. [Citation Graph (, )][DBLP]


  25. EMORBFN: An Evolutionary Multiobjetive Optimization Algorithm for RBFN Design. [Citation Graph (, )][DBLP]


  26. Designing Radial Basis Function Neural Networks with Meta-Evolutionary Algorithms: The Effect of Chromosome Codification. [Citation Graph (, )][DBLP]


  27. Learning compact fuzzy rule-based classification systems with genetic programming. [Citation Graph (, )][DBLP]


  28. Genetic Cooperative-Competitive Fuzzy Rule Based Learning Method using Genetic Programming for Highly Imbalanced Data-Sets. [Citation Graph (, )][DBLP]


  29. Automatic Neural Net Design by Means of a Symbiotic Co-evolutionary Algorithm. [Citation Graph (, )][DBLP]


  30. Non-dominated Multi-objective Evolutionary Algorithm Based on Fuzzy Rules Extraction for Subgroup Discovery. [Citation Graph (, )][DBLP]


  31. Techniques of Engineering Applied to a Non-structured Data Model. [Citation Graph (, )][DBLP]


  32. A Symbiotic CHC Co-evolutionary Algorithm for Automatic RBF Neural Networks Design. [Citation Graph (, )][DBLP]


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