Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction

  1. De Mendizabal, Iñaki Velez 1
  2. Fernandes, Vitor Basto 2
  3. Ezpeleta, Enaitz 1
  4. Reboredo, José Ramón Méndez 3
  5. Meire, Silvana Gómez 3
  6. Zurutuza, Urko 1
  1. 1 Universidad de Mondragón/Mondragon Unibertsitatea
    info

    Universidad de Mondragón/Mondragon Unibertsitatea

    Mondragón, España

    ROR https://ror.org/00wvqgd19

  2. 2 Instituto Universitário de Lisboa (ISCTE-IUL)
  3. 3 Universidade de Vigo
    info

    Universidade de Vigo

    Vigo, España

    ROR https://ror.org/05rdf8595

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Editor: Zenodo

Ano de publicación: 2022

Tipo: Dataset

CC BY 4.0

DOI: 10.5281/ZENODO.7441851 lock_openAcceso aberto editor

Resumo

Multi-Objective Evolutioary Algorithms for Synset Dimensionality Reduction This work has been developed to discover the usage of Multi-Objective Evolutionary computation to reduce the dimensionality of synset-based datatsets. The objective of this code is to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using Multi-Objective Evolutionary Algorithms (MOEA).