IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0196385.html
   My bibliography  Save this article

An efficient approach for feature construction of high-dimensional microarray data by random projections

Author

Listed:
  • Hassan Tariq
  • Elf Eldridge
  • Ian Welch

Abstract

Dimensionality reduction of microarray data is a very challenging task due to high computational time and the large amount of memory required to train and test a model. Genetic programming (GP) is a stochastic approach to solving a problem. For high dimensional datasets, GP does not perform as well as other machine learning algorithms. To explore the inherent property of GP to generalize models from low dimensional data, we need to consider dimensionality reduction approaches. Random projections (RPs) have gained attention for reducing the dimensionality of data with reduced computational cost, compared to other dimensionality reduction approaches. We report that the features constructed from RPs perform extremely well when combined with a GP approach. We used eight datasets out of which seven have not been reported as being used in any machine learning research before. We have also compared our results by using the same full and constructed features for decision trees, random forest, naive Bayes, support vector machines and k-nearest neighbor methods.

Suggested Citation

  • Hassan Tariq & Elf Eldridge & Ian Welch, 2018. "An efficient approach for feature construction of high-dimensional microarray data by random projections," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-8, April.
  • Handle: RePEc:plo:pone00:0196385
    DOI: 10.1371/journal.pone.0196385
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0196385
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0196385&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0196385?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
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Abhijeet R Patil & Sangjin Kim, 2020. "Combination of Ensembles of Regularized Regression Models with Resampling-Based Lasso Feature Selection in High Dimensional Data," Mathematics, MDPI, vol. 8(1), pages 1-23, January.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0196385. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.