Publikationen in Zusammenarbeit mit Forschern von King Abdulaziz University (7)

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

2019

  1. 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. 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

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

2014

  1. LI-MLC: A label inference methodology for addressing high dimensionality in the label space for multilabel classification

    IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, Núm. 10, pp. 1842-1854