Publicaciones en las que colabora con Francisco Herrera Triguero (28)

2022

  1. Reducing Data Complexity Using Autoencoders With Class-Informed Loss Functions

    IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 44, Núm. 12, pp. 9549-9560

2021

  1. Slicer: Feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study

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

  2. Slicer: feature Learning for Class Separability with Least-Squares Support Vector Machine Loss and COVID-19 Chest X-Ray Case Study

    Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings

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

  2. Artificial intelligence within the interplay between natural and artificial computation: Advances in data science, trends and applications

    Neurocomputing, Vol. 410, pp. 237-270

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. A snapshot on nonstandard supervised learning problems: taxonomy, relationships, problem transformations and algorithm adaptations

    Progress in Artificial Intelligence, Vol. 8, Núm. 1

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

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

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

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

  5. Ruta: Implementations of neural autoencoders in R

    Knowledge-Based Systems, Vol. 174, pp. 4-8

  6. Smartdata: Data preprocessing to achieve smart data in R

    Neurocomputing, Vol. 360, pp. 1-13

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. 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. A first approach towards a fuzzy decision tree for multilabel classification

    IEEE International Conference on Fuzzy Systems

2016

  1. Multilabel classification: Problem analysis, metrics and techniques

    Springer International Publishing, pp. 1-194

  2. 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)

  3. 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. MLSMOTE: Approaching imbalanced multilabel learning through synthetic instance generation

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