IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v54y2010i5p1247-1265.html
   My bibliography  Save this article

Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications

Author

Listed:
  • Lee, Namgil
  • Kim, Jong-Min

Abstract

Many pattern classification algorithms such as Support Vector Machines (SVMs), Multi-Layer Perceptrons (MLPs), and K-Nearest Neighbors (KNNs) require data to consist of purely numerical variables. However many real world data consist of both categorical and numerical variables. In this paper we suggest an effective method of converting the mixed data of categorical and numerical variables into data of purely numerical variables for binary classifications. Since the suggested method is based on the theory of learning Bayesian Network Classifiers (BNCs), it is computationally efficient and robust to noises and data losses. Also the suggested method is expected to extract sufficient information for estimating a minimum-error-rate (MER) classifier. Simulations on artificial data sets and real world data sets are conducted to demonstrate the competitiveness of the suggested method when the number of values in each categorical variable is large and BNCs accurately model the data.

Suggested Citation

  • Lee, Namgil & Kim, Jong-Min, 2010. "Conversion of categorical variables into numerical variables via Bayesian network classifiers for binary classifications," Computational Statistics & Data Analysis, Elsevier, vol. 54(5), pages 1247-1265, May.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:5:p:1247-1265
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(09)00406-X
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

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

    More about this item

    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:eee:csdana:v:54:y:2010:i:5:p:1247-1265. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.