Publikationen, an denen er mitarbeitet SERGIO GONZÁLEZ VÁZQUEZ (12)

2021

  1. Fuzzy k-nearest neighbors with monotonicity constraints: Moving towards the robustness of monotonic noise

    Neurocomputing, Vol. 439, pp. 106-121

2020

  1. A practical tutorial on bagging and boosting based ensembles for machine learning: Algorithms, software tools, performance study, practical perspectives and opportunities

    Information Fusion, Vol. 64, pp. 205-237

  2. Preprocessing methodology for time series: An industrial world application case study

    Information Sciences, Vol. 514, pp. 385-401

2019

  1. Chain based sampling for monotonic imbalanced classification

    Information Sciences, Vol. 474, pp. 187-204

2018

  1. "k"-vecinos más cercanos difuso para clasificación monotónica

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

  2. DRCW-ASEG: One-versus-One distance-based relative competence weighting with adaptive synthetic example generation for multi-class imbalanced datasets

    Neurocomputing, Vol. 285, pp. 176-187

2017

  1. Class Switching according to Nearest Enemy Distance for learning from highly imbalanced data-sets

    Pattern Recognition, Vol. 70, pp. 12-24

  2. Evolutionary Fuzzy Rule-Based Methods for Monotonic Classification

    IEEE Transactions on Fuzzy Systems, Vol. 25, Núm. 6, pp. 1376-1390

  3. 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

2016

  1. Managing monotonicity in classification by a pruned adaboost

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

2015

  1. Managing monotonicity in classification by a pruned random forest

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

  2. Monotonic Random Forest with an Ensemble Pruning Mechanism based on the Degree of Monotonicity

    New Generation Computing, Vol. 33, Núm. 4, pp. 367-388