Publicaciones en colaboración con investigadores/as de Universidad Pública de Navarra (28)

2019

  1. Guest Editorial: Computational Intelligence for Big Data Analytics

    Cognitive Computation

2018

  1. Improving fuzzy rule based classification systems in big data via support-based filtering

    IEEE International Conference on Fuzzy Systems

  2. NMC, Nearest Matrix Classification: a new combination model for pruning one-vs-one ensembles by transforming the aggregation problem

    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. 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

2016

  1. 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

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

    Advances in Intelligent Systems and Computing

  3. 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. 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

  2. Enhancing Multiclass Classification in FARC-HD Fuzzy Classifier: On the Synergy Between n-Dimensional Overlap Functions and Decomposition Strategies

    IEEE Transactions on Fuzzy Systems, Vol. 23, Núm. 5, pp. 1562-1580

  3. On the impact of Distance-based Relative Competence Weighting approach in One-vs-One classification for Evolutionary Fuzzy Systems: DRCW-FH-GBML algorithm

    IEEE International Conference on Fuzzy Systems

2014

  1. Empowering difficult classes with a similarity-based aggregation in multi-class classification problems

    Information Sciences, Vol. 264, pp. 135-157

  2. Enhancing difficult classes in one-vs-one classifier fusion strategy using restricted equivalence functions

    FUSION 2014 - 17th International Conference on Information Fusion

2013

  1. Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches

    Knowledge-Based Systems, Vol. 42, pp. 97-110

  2. Dynamic classifier selection for One-vs-One strategy: Avoiding non-competent classifiers

    Pattern Recognition, Vol. 46, Núm. 12, pp. 3412-3424

  3. EUSBoost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling

    Pattern Recognition, Vol. 46, Núm. 12, pp. 3460-3471

  4. IVTURS: A linguistic fuzzy rule-based classification system based on a new interval-valued fuzzy reasoning method with tuning and rule selection

    IEEE Transactions on Fuzzy Systems, Vol. 21, Núm. 3, pp. 399-411

2012

  1. A review on ensembles for the class imbalance problem: Bagging-, boosting-, and hybrid-based approaches

    IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews, Vol. 42, Núm. 4, pp. 463-484

  2. IIVFDT: Ignorance functions based interval-valued fuzzy decision tree with genetic tuning

    International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, Vol. 20, Núm. SUPPL. 2, pp. 1-30

2011

  1. A case study on medical diagnosis of cardiovascular diseases using a genetic algorithm for tuning fuzzy rule-based classification systems with interval-valued fuzzy sets

    IEEE SSCI 2011: Symposium Series on Computational Intelligence - T2FUZZ 2011: 2011 IEEE Symposium on Advances in Type-2 Fuzzy Logic Systems

  2. A genetic tuning to improve the performance of fuzzy rule-based classification systems with interval-valued fuzzy sets: Degree of ignorance and lateral position

    International Journal of Approximate Reasoning, Vol. 52, Núm. 6, pp. 751-766