IDEAS home Printed from https://ideas.repec.org/h/spr/advbcp/978-94-6463-218-7_8.html

Imputation Algorithm for Multi-view Financial Data Based on Weighted Random Forest

In: Proceedings of the 2023 2nd International Conference on Urban Planning and Regional Economy (UPRE 2023)

Author

Listed:
  • Jun Cao

    (Jiangnan University, College of Artificial Intelligence and Computer Science)

  • Fanyu Wang

    (Jiangnan University, College of Artificial Intelligence and Computer Science)

  • Zhenping Xie

    (Jiangnan University, College of Artificial Intelligence and Computer Science)

  • She Song

    (Inspur Zhuoshu Big Data Industry Development Company Limited)

Abstract

With the development of information technology, a large amount of multi-view data continues to emerge in the financial field. The absence of these multi-view data samples limits the research processing of financial data, while the popular single-view filling algorithm cannot handle the problem of missing multi-view data well. To address this problem, this study proposes a new filling method called Weighted Multi-view Random Forest (WMVRF), which innovatively combines feature importance to calculate view weights and enables missing filling of multi-view data by integrating the label prediction results from multiple views random forests. Several filling algorithms such as MissForest, Generative Adversarial Imputation Network, and KNN are compared on one real dataset and four multi-view public datasets (Handwritten, Webkb, 3Sources, BBCSport). The experimental results show that the proposed method reduces the normalized root mean square error (NRMSE) by 1.6% and outperforms the KNN, GAIN, and EM filling algorithms on the financial dataset compared to RF.

Suggested Citation

  • Jun Cao & Fanyu Wang & Zhenping Xie & She Song, 2023. "Imputation Algorithm for Multi-view Financial Data Based on Weighted Random Forest," Advances in Economics, Business and Management Research, in: Reza Lotfi & Chukwunonso Kelvin Oraedu & Ferdous Ahmed (ed.), Proceedings of the 2023 2nd International Conference on Urban Planning and Regional Economy (UPRE 2023), pages 55-70, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-218-7_8
    DOI: 10.2991/978-94-6463-218-7_8
    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:advbcp:978-94-6463-218-7_8. 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.