IDEAS home Printed from https://ideas.repec.org/a/ids/ijpdev/v25y2021i2p101-113.html
   My bibliography  Save this article

Error correction method of enterprise product cost accounting based on machine learning algorithm

Author

Listed:
  • Hui Zhou

Abstract

In order to solve the problems of large error correction result and long-time consumption in traditional cost accounting methods, an error correction method of enterprise product cost accounting based on machine learning algorithm is proposed. By analysing the specific cost chain and accounting error sources of enterprise product production, the influence of uncontrollable factors on accounting error is preliminarily avoided. On this basis, the total error of cost accounting is determined by constructing error measurement model. Finally, the optimal solution of error calculation is obtained by using the support vector machine concept in machine learning algorithm, so as to realise error correction. The experimental results show that the correction time consumption of the proposed method is short, the accuracy is high, and the memory space is small, which indicates that the method has a certain application value and can be widely used in the field of enterprise cost accounting.

Suggested Citation

  • Hui Zhou, 2021. "Error correction method of enterprise product cost accounting based on machine learning algorithm," International Journal of Product Development, Inderscience Enterprises Ltd, vol. 25(2), pages 101-113.
    • Handle: RePEc:ids:ijpdev:v:25:y:2021:i:2:p:101-113
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=116148
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijpdev:v:25:y:2021:i:2:p:101-113. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=36 .

    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.