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Improving Large-Scale k -Nearest Neighbor Text Categorization with Label Autoencoders

Author

Listed:
  • Francisco J. Ribadas-Pena

    (Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
    These authors contributed equally to this work.)

  • Shuyuan Cao

    (Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
    These authors contributed equally to this work.)

  • Víctor M. Darriba Bilbao

    (Department of Computer Science, University of Vigo, Edificio Politécnico, Campus As Lagoas s/n, 32004 Ourense, Spain
    These authors contributed equally to this work.)

Abstract

In this paper, we introduce a multi-label lazy learning approach to deal with automatic semantic indexing in large document collections in the presence of complex and structured label vocabularies with high inter-label correlation. The proposed method is an evolution of the traditional k -Nearest Neighbors algorithm which uses a large autoencoder trained to map the large label space to a reduced size latent space and to regenerate the predicted labels from this latent space. We have evaluated our proposal in a large portion of the MEDLINE biomedical document collection which uses the Medical Subject Headings (MeSH) thesaurus as a controlled vocabulary. In our experiments we propose and evaluate several document representation approaches and different label autoencoder configurations.

Suggested Citation

  • Francisco J. Ribadas-Pena & Shuyuan Cao & Víctor M. Darriba Bilbao, 2022. "Improving Large-Scale k -Nearest Neighbor Text Categorization with Label Autoencoders," Mathematics, MDPI, vol. 10(16), pages 1-22, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2867-:d:885735
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    References listed on IDEAS

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    1. Grigorios Tsoumakas & Ioannis Katakis, 2007. "Multi-Label Classification: An Overview," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 3(3), pages 1-13, July.
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