IDEAS home Printed from https://ideas.repec.org/a/wsi/jikmxx/v19y2020i04ns0219649220500392.html
   My bibliography  Save this article

Text Categorisation Through Dimensionality Reduction Using Wavelet Transform

Author

Listed:
  • Jorge Chamorro-Padial

    (Departamento de Ciencias de la Computación e Inteligencia Artificial, CITIC-UGR Universidad de Granada, 18071 Granada, Spain)

  • Rosa Rodríguez-Sánchez

    (Departamento de Ciencias de la Computación e Inteligencia Artificial, CITIC-UGR Universidad de Granada, 18071 Granada, Spain)

Abstract

This paper proposes a new method of dimensionality reduction when performing Text Classification, by applying the discrete wavelet transform to the document-term frequencies matrix. We analyse the features provided by the wavelet coefficients from the different orientations: (1) The high energy coefficients in the horizontal orientation correspond to relevant terms in a single document. (2) The high energy coefficients in the vertical orientation correspond to relevant terms for a single document, but not for the others. (3) The high energy coefficients in the diagonal orientation correspond to relevant terms in a document in comparison to other terms. If we filter using the wavelet coefficients and fulfil these three conditions simultaneously, we can obtain a reduced vocabulary of the corpus, with less dimensions than in the original one. To test the success of the reduced vocabulary, we recoded the corpus with the new reduced vocabulary and we obtained a statistically relevant level of accuracy for document classification.

Suggested Citation

  • Jorge Chamorro-Padial & Rosa Rodríguez-Sánchez, 2020. "Text Categorisation Through Dimensionality Reduction Using Wavelet Transform," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 19(04), pages 1-21, December.
    • Handle: RePEc:wsi:jikmxx:v:19:y:2020:i:04:n:s0219649220500392
    DOI: 10.1142/S0219649220500392
    as

    Download full text from publisher

    File URL: http://www.worldscientific.com/doi/abs/10.1142/S0219649220500392
    Download Restriction: Access to full text is restricted to subscribers
    File URL: https://libkey.io/10.1142/S0219649220500392?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wsi:jikmxx:v:19:y:2020:i:04:n:s0219649220500392. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Tai Tone Lim (email available below). General contact details of provider: http://www.worldscinet.com/jikm/jikm.shtml .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.