• Login
    View Item 
    •   Research Bank Home
    • Unitec Institute of Technology
    • Study Areas
    • Computing
    • Computing Journal Articles
    • View Item
    •   Research Bank Home
    • Unitec Institute of Technology
    • Study Areas
    • Computing
    • Computing Journal Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Technical review : performance of existing imputation methods for missing data in SVM ensemble creation

    Ali, Shahid; Dacey, Simon

    Thumbnail
    Share
    View fulltext online
    Ali, S. (2017).pdf (624.8Kb)
    Date
    2017
    Citation:
    Ali, S., & Dacey, S. (2017). Technical Review: Performance of Existing Imputation Methods for Missing Data in SVM Ensemble Creation. International Journal of Data Mining & Knowledge Management Process (IJDKP), 7(6), 75-91. doi:10.5121/ijdkp.2017.7606
    Permanent link to Research Bank record:
    https://hdl.handle.net/10652/4342
    Abstract
    Incomplete data is present in many study contents. This incomplete or uncollected data information is named as missing data (values), and considered as vital problem for various researchers. Even this missing data problem is faced more in air pollution monitoring stations, where data is collected from multiple monitoring stations widespread across various locations. In literature, various imputation methods for missing data are proposed, however, in this research we considered only existing imputation methods for missing data and recorded their performance in ensemble creation. The five existing imputation methods for missing data deployed in this research are series mean method, mean of nearby points, median of nearby points, linear trend at a point and linear interpolation respectively. Series mean (SM) method demonstrated comparatively better to other imputation methods with least mean absolute error and better performance accuracy for SVM ensemble creation on CO data set using bagging and boosting algorithms.
    Keywords:
    missing data problem, ensemble learning, imputation methods, series mean (SM) method, support vector machine (SVM), bootstrap aggregating (meta-algorithm), bagging (meta-algorithm), boosting (meta-algorithm), aggregation (machine learning), air pollution analysis, SVM
    ANZSRC Field of Research:
    170203 Knowledge Representation and Machine Learning
    Copyright Holder:
    Authors

    Copyright Notice:
    All rights reserved
    Available Online at:
    http://aircconline.com/ijdkp/V7N6/7617ijdkp06.pdf
    Rights:
    This digital work is protected by copyright. It may be consulted by you, provided you comply with the provisions of the Act and the following conditions of use. These documents or images may be used for research or private study purposes. Whether they can be used for any other purpose depends upon the Copyright Notice above. You will recognise the author's and publishers rights and give due acknowledgement where appropriate.
    Metadata
    Show detailed record
    This item appears in
    • Computing Journal Articles [51]

    Te Pūkenga

    Research Bank is part of Te Pūkenga - New Zealand Institute of Skills and Technology

    • About Te Pūkenga
    • Privacy Notice

    Copyright ©2022 Te Pūkenga

    Usage

    Downloads, last 12 months
    42
     
     

    Usage Statistics

    For this itemFor the Research Bank

    Share

    About

    About Research BankContact us

    Help for authors  

    How to add research

    Register for updates  

    LoginRegister

    Browse Research Bank  

    EverywhereInstitutionsStudy AreaAuthorDateSubjectTitleType of researchSupervisorCollaboratorThis CollectionStudy AreaAuthorDateSubjectTitleType of researchSupervisorCollaborator

    Te Pūkenga

    Research Bank is part of Te Pūkenga - New Zealand Institute of Skills and Technology

    • About Te Pūkenga
    • Privacy Notice

    Copyright ©2022 Te Pūkenga