Multi-objective evolutionary optimization for dimensionality reduction of texts represented by synsets

  1. Vélez de Mendizabal, Iñaki 15
  2. Basto-Fernandes, Vitor 1
  3. Ezpeleta, Enaitz 5
  4. Méndez, José R. 234
  5. Gómez-Meire, Silvana 4
  6. Zurutuza, Urko 5
  1. 1 University Institute of Lisbon ISTAR-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), Lisboa, Portugal
  2. 2 Galicia Sur Health Research Institute (IIS Galicia Sur), Hospital Álvaro Cunqueiro, Bloque técnico, SING Research Group, Vigo, Pontevedra, Spain
  3. 3 CINBIO-Biomedical Research Centre, Lagoas-Marcosende, Vigo, Pontevedra, Spain
  4. 4 Department of Computer Science Universidade de Vigo, Ourense, Spain
  5. 5 Universidad de Mondragón/Mondragon Unibertsitatea
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    Universidad de Mondragón/Mondragon Unibertsitatea

    Mondragón, España

    ROR https://ror.org/00wvqgd19

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Revista:
PeerJ Computer Science

ISSN: 2376-5992

Ano de publicación: 2023

Volume: 9

Páxinas: e1240

Tipo: Artigo

DOI: 10.7717/PEERJ-CS.1240 GOOGLE SCHOLAR lock_openAcceso aberto editor

Outras publicacións en: PeerJ Computer Science

Obxectivos de Desenvolvemento Sustentable

Resumo

Despite new developments in machine learning classification techniques, improving the accuracy of spam filtering is a difficult task due to linguistic phenomena that limit its effectiveness. In particular, we highlight polysemy, synonymy, the usage of hypernyms/hyponyms, and the presence of irrelevant/confusing words. These problems should be solved at the pre-processing stage to avoid using inconsistent information in the building of classification models. Previous studies have suggested that the use of synset-based representation strategies could be successfully used to solve synonymy and polysemy problems. Complementarily, it is possible to take advantage of hyponymy/hypernymy-based to implement dimensionality reduction strategies. These strategies could unify textual terms to model the intentions of the document without losing any information (e.g., bringing together the synsets “viagra”, “ciallis”, “levitra” and other representing similar drugs by using “virility drug” which is a hyponym for all of them). These feature reduction schemes are known as lossless strategies as the information is not removed but only generalised. However, in some types of text classification problems (such as spam filtering) it may not be worthwhile to keep all the information and let dimensionality reduction algorithms discard information that may be irrelevant or confusing. In this work, we are introducing the feature reduction as a multi-objective optimisation problem to be solved using a Multi-Objective Evolutionary Algorithm (MOEA). Our algorithm allows, with minor modifications, to implement lossless (using only semantic-based synset grouping), low-loss (discarding irrelevant information and using semantic-based synset grouping) or lossy (discarding only irrelevant information) strategies. The contribution of this study is two-fold: (i) to introduce different dimensionality reduction methods (lossless, low-loss and lossy) as an optimization problem that can be solved using MOEA and (ii) to provide an experimental comparison of lossless and low-loss schemes for text representation. The results obtained support the usefulness of the low-loss method to improve the efficiency of classifiers.

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Información de financiamento

Financiadores

  • SMEIC, SRA and ERDF
    • TIN2017-84658-C2-1-R and TIN2017-84658-C2-2-R
  • Conselleria de Cultura, Educación e Universidade of Xunta de Galicia
    • ED431C 2022/03-GRC
  • Universities and Research of the Basque Country
    • IT1676-22
  • FCT
    • UIDB/04466/2020 and UIDP/04466/2020

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