• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Research Units
  • KINDI Center for Computing Research
  • Network & Distributed Systems
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Research Units
  • KINDI Center for Computing Research
  • Network & Distributed Systems
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    UniBFS: A novel uniform-solution-driven binary feature selection algorithm for high-dimensional data

    View/Open
    Publisher version (You have accessOpen AccessIcon)
    Publisher version (Check access options)
    Check access options
    1-s2.0-S2210650224002530-main.pdf (4.433Mb)
    Date
    2024
    Author
    Ahadzadeh, Behrouz
    Abdar, Moloud
    Foroumandi, Mahdieh
    Safara, Fatemeh
    Khosravi, Abbas
    García, Salvador
    Suganthan, Ponnuthurai Nagaratnam
    ...show more authors ...show less authors
    Metadata
    Show full item record
    Abstract
    Feature selection (FS) is a crucial technique in machine learning and data mining, serving a variety of purposes such as simplifying model construction, facilitating knowledge discovery, improving computational efficiency, and reducing memory consumption. Despite its importance, the constantly increasing search space of high-dimensional datasets poses significant challenges to FS methods, including issues like the "curse of dimensionality," susceptibility to local optima, and high computational and memory costs. To overcome these challenges, a new FS algorithm named Uniform-solution-driven Binary Feature Selection (UniBFS) has been developed in this study. UniBFS exploits the inherent characteristic of binary algorithms-binary coding-to search the entire problem space for identifying relevant features while avoiding irrelevant ones. To improve the effectiveness and efficiency of the UniBFS algorithm, Redundant Features Elimination algorithm (RFE) is presented in this paper. RFE performs a local search in a very small subspace of the solutions obtained by UniBFS in different stages, and removes the redundant features which do not increase the classification accuracy. Moreover, the study proposes a hybrid algorithm that combines UniBFS with two filter-based FS methods, ReliefF and Fisher, to identify pertinent features during the global search phase. The proposed algorithms are evaluated on 30 high-dimensional datasets ranging from 2000 to 54676 dimensions, and their effectiveness and efficiency are compared with state-of-the-art techniques, demonstrating their superiority. 2024 The Author(s)
    DOI/handle
    http://dx.doi.org/10.1016/j.swevo.2024.101715
    http://hdl.handle.net/10576/62214
    Collections
    • Network & Distributed Systems [‎142‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video