Publicaciones en las que colabora con Francisco Herrera Triguero (86)

2022

  1. Una visión actual de la inteligencia artificial: Recorrido histórico, datos y aprendizaje, confiabilidad y datos

    El derecho y la inteligencia artificial (Editorial Universidad de Granada), pp. 51-80

2020

  1. An analysis on the use of autoencoders for representation learning: Fundamentals, learning task case studies, explainability and challenges

    Neurocomputing, Vol. 404, pp. 93-107

2019

  1. A Showcase of the Use of Autoencoders in Feature Learning Applications

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

  2. Dealing with difficult minority labels in imbalanced mutilabel data sets

    Neurocomputing, Vol. 326-327, pp. 39-53

  3. 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

  4. REMEDIAL-HwR: Tackling multilabel imbalance through label decoupling and data resampling hybridization

    Neurocomputing, Vol. 326-327, pp. 110-122

2018

  1. A practical tutorial on autoencoders for nonlinear feature fusion: Taxonomy, models, software and guidelines

    Information Fusion, Vol. 44, pp. 78-96

  2. A practical tutorial on autoencoders for nonlinear feature fusion: taxonomy, models, software and guidelines

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

  3. A unifying analysis for the supervised descriptive rule discovery via the weighted relative accuracy

    Knowledge-Based Systems, Vol. 139, pp. 89-100

  4. Atipicidad: medida de calidad clave dentro del descubrimiento de reglas descriptivas supervisadas

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

  5. 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

  6. Tips, guidelines and tools for managing multi-label datasets: The mldr.datasets R package and the Cometa data repository

    Neurocomputing, Vol. 289, pp. 68-85

2017

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

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

  2. Multilabel classification: Problem analysis, metrics and techniques

    Springer International Publishing, pp. 1-194

  3. On the impact of dataset complexity and sampling strategy in multilabel classifiers performance

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

  4. R ultimate multilabel dataset repository

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

2015

  1. Addressing imbalance in multilabel classification: Measures and random resampling algorithms

    Neurocomputing, Vol. 163, pp. 3-16

  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. MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

    Knowledge-Based Systems, Vol. 89, pp. 385-397