Mikel Galar Idoate-rekin lankidetzan egindako argitalpenak (13)

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. Improving Pairwise Learning Classification in Fuzzy Rule Based Classification Systems Using Dynamic Classifier Selection

    Proceedings of the 2015 conference of the international fuzzy systems association and the european society for fuzzy logic and technology

  4. 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