IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-88530-4_4.html
   My bibliography  Save this book chapter

Contextual Mobile Datasets, Pre-processing and Feature Selection

In: Context-Aware Machine Learning and Mobile Data Analytics

Author

Listed:
  • Iqbal H. Sarker

    (Swinburne University of Technology
    Chittagong University of Engineering & Technology)

  • Alan Colman

    (Swinburne University of Technology)

  • Jun Han

    (Swinburne University of Technology)

  • Paul Watters

    (Macquarie University
    Cyberstronomy Pty Ltd)

Abstract

Context-aware computing has been explored by many research communities and industries for various applications. In the earlier chapters, we have presented various components of context-aware machine learning framework and systems with their related issues, where contextual data acquisition is the primary step for context-aware machine learning modeling. In this chapter, we present several contextual datasets that can be utilized to build a machine learning based context-aware model for corresponding mobile applications and services. As the real-world data may contain noisy and inconsistency instances, the pre-processing steps have also been analyzed to clean and remove noises from raw data. Finally, the basic feature selection and extraction methods for efficient processing has also been provided in this chapter.

Suggested Citation

  • Iqbal H. Sarker & Alan Colman & Jun Han & Paul Watters, 2021. "Contextual Mobile Datasets, Pre-processing and Feature Selection," Springer Books, in: Context-Aware Machine Learning and Mobile Data Analytics, chapter 0, pages 59-73, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-88530-4_4
    DOI: 10.1007/978-3-030-88530-4_4
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:spr:sprchp:978-3-030-88530-4_4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.