Francisco Herrera Triguero-rekin lankidetzan egindako argitalpenak (56)

2019

  1. Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?

    IEEE Computational Intelligence Magazine, Vol. 14, Núm. 1, pp. 69-81

2018

  1. Pareto based ensemble with feature and instance selection for learning from multi-class imbalanced datasets

    XVIII Conferencia de la Asociación Española para la Inteligencia Artificial (CAEPIA 2018): avances en Inteligencia Artificial. 23-26 de octubre de 2018 Granada, España

2017

  1. A pareto-based ensemble with feature and instance selection for learning from multi-class imbalanced datasets

    International Journal of Neural Systems, Vol. 27, Núm. 6

  2. A review of distributed data models for learning

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. An insight into imbalanced Big Data classification: outcomes and challenges

    COMPLEX & INTELLIGENT SYSTEMS, Vol. 3, Núm. 2, pp. 105-120

  4. Chi-Spark-RS: An Spark-built evolutionary fuzzy rule selection algorithm in imbalanced classification for big data problems

    IEEE International Conference on Fuzzy Systems

  5. Fuzzy rule based classification systems for big data with MapReduce: granularity analysis

    Advances in Data Analysis and Classification, Vol. 11, Núm. 4, pp. 711-730

  6. KEEL 3.0: An Open Source Software for Multi-Stage Analysis in Data Mining

    International Journal of Computational Intelligence Systems, Vol. 10, Núm. 1, pp. 1238-1249

  7. NMC: nearest matrix classification – A new combination model for pruning One-vs-One ensembles by transforming the aggregation problem

    Information Fusion, Vol. 36, pp. 26-51

  8. Why Linguistic Fuzzy Rule Based Classification Systems perform well in Big Data Applications?

    International Journal of Computational Intelligence Systems, Vol. 10, Núm. 1, pp. 1211-1225

2016

  1. A View on Fuzzy Systems for Big Data: Progress and Opportunities

    International Journal of Computational Intelligence Systems, Vol. 9, pp. 69-80

  2. A first approach in evolutionary fuzzy systems based on the lateral tuning of the linguistic labels for big data classification

    2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

  3. Enhancing evolutionary fuzzy systems for multi-class problems: Distance-based relative competence weighting with truncated confidences (DRCW-TC)

    International Journal of Approximate Reasoning, Vol. 73, pp. 108-122

  4. Evolutionary fuzzy systems: A case study in imbalanced classification

    Studies in Fuzziness and Soft Computing (Springer Verlag), pp. 169-200

  5. New ordering-based pruning metrics for ensembles of classifiers in imbalanced datasets

    Advances in Intelligent Systems and Computing

  6. On the combination of pairwise and granularity learning for improving fuzzy rule-based classification systems: GL-FARCHD-OVO

    Advances in Intelligent Systems and Computing

  7. Ordering-based pruning for improving the performance of ensembles of classifiers in the framework of imbalanced datasets

    Information Sciences, Vol. 354, pp. 178-196

2015

  1. A proposal for evolutionary fuzzy systems using feature weighting: Dealing with overlapping in imbalanced datasets

    Knowledge-Based Systems, Vol. 73, pp. 1-17

  2. Addressing overlapping in classification with imbalanced datasets: A first multi-objective approach for feature and instance selection

    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

  3. DRCW-OVO: Distance-based relative competence weighting combination for One-vs-One strategy in multi-class problems

    Pattern Recognition, Vol. 48, Núm. 1, pp. 28-42